Data covers ERC-8004 agent registrations and Agent Trust Score assessments. Week 35 = week ending Sunday, August 30, 2026.

Week 35 registrations eased slightly after last week’s record surge. 29,601 new agents registered – down 7.4% from Week 34’s all-time high of 31,979, but still the second-highest weekly total on record. Unique owner wallets fell more sharply, down 32.1% to 18,344, pushing the agents-per-owner ratio up to 1.61 – the highest concentration level since Week 31’s fleet-registration spike. Median trust score held essentially flat, up 1.8% to 573, comfortably inside the Provisional tier. The full indexed agent population now stands at 470,961, up 51,179 from last week – notably more than this week’s own registration count, suggesting some of the increase reflects backfilled registrations from prior weeks rather than new activity alone.

Week 35 Key Numbers

Week 35 Analysis

Registrations Cool Slightly From an All-Time High

29,601 new registrations is down modestly from Week 34’s record 31,979, but still comfortably the second-highest weekly total the tracker has recorded, well above the 10,000-20,000 range that characterized most of July and early August. Unlike Week 34, where registrations and owners moved in near-lockstep, this week’s owner count fell much faster than registrations – a divergence worth watching closely next week.

Agents-Per-Owner Ratio Climbs to Its Highest Level Since Week 31

At 1.61 agents per owner, Week 35 marks a clear jump from Week 34’s 1.18 and the highest ratio recorded since Week 31’s 2.54 fleet-registration spike. Because registrations fell only modestly while unique owners dropped by nearly a third, this week’s activity looks more concentrated among fewer distinct wallets than last week’s broad-based surge – though at 1.61 it remains far short of Week 31’s more extreme concentration level.

Median Trust Score Holds Steady

Median trust score ticked up slightly to 573 from 563, essentially flat week-over-week and continuing to sit near the best levels recorded since Week 26. Unlike prior instances where rising concentration (higher agents-per-owner) coincided with falling trust quality, this week’s uptick in concentration did not come with a corresponding dip in trust – a combination that breaks from the pattern seen in Week 31, when a similar concentration spike coincided with the lowest median trust score on record (200).

Week-on-Week Comparison: W33 – W35

2026 Weekly Registration History – W29 to W35

Note: Median trust score reflects the median Agent Trust Score of agents registered in that specific week, not the cumulative population median. The trust flag breakdown below covers the full indexed population (470,961 agents) as of Week 35, not just this week’s registrations. The population increase this week (51,179) exceeds this week’s own registration count (29,601), indicating some backfilled registrations from prior weeks are included in the total.

Trust Flag Breakdown: What the Full Agent Population Looks Like

The full indexed agent population now stands at 470,961 agents across eight chains, up from 419,782 last week. Here is the updated trust flag breakdown across that entire population.

Sybil-farm flagged agents continued easing as a share of the population, from 17.05% to 15.41% – the third consecutive week of decline in this metric even as the absolute count rose to 72,555 (+981). FEEDER_UNKNOWN followed the same pattern, falling from 7.82% to 7.36% of the population despite an absolute increase of 1,836 agents. This continues the trend seen last week: a growing population combined with a shrinking farm-flagged share, consistent with the recent registration waves skewing toward more broadly distributed, less farm-like activity.

The most serious flags remain rare and largely stable in absolute terms: 774 agents have an owner funded by a confirmed rug pull operator (up 5 from last week), and 61 agents are controlled directly by a wallet with a confirmed rug pull history (unchanged). Confirmed CEX-funded owners rose slightly to 2,825 agents but slipped as a population share to 0.60%. Honeypot-linked flags remain at zero, and the structural multisig-council flags remain negligible.

About This Tracker

ChainAware.ai’s weekly ERC-8004 agent tracker monitors new agent registrations on the ERC-8004 Identity Registry, the number of unique owner wallets behind those registrations, and the median Agent Trust Score of each week’s registration cohort. Agent Trust Score is a 0-1000 score combining owner wallet fraud probability, feeder address analysis, farm detection, and criminal record checks – see the full Agent Trust Score methodology for how the model scores agents and their controlling wallets. Data is updated weekly alongside ChainAware’s PancakeSwap/Uniswap rug pull tracker.

The post ERC-8004 Agent Trust Tracker Week 35 2026 – Registrations Ease as Agents-Per-Owner Ratio Climbs to 1.61 first appeared on ChainAware.ai.

Data covers ERC-8004 agent registrations and Agent Trust Score assessments. Week 34 = week ending Sunday, August 23, 2026.

Week 34 delivered the sharpest rebound in the tracker’s history. 31,979 new agents registered – up 208.6% from Week 33’s 10,361, and the highest weekly registration total we have recorded to date, surpassing the prior peak of 29,577 in Week 27. Unique owner wallets nearly tripled as well, up 184.2% to 27,033. Median trust score climbed 19.0% to 563, its highest level since Week 26’s 644 and comfortably inside the Provisional tier. The full indexed agent population now stands at 419,782, up 32,527 from last week – almost exactly matching this week’s own registration count.

Week 34 Key Numbers

Week 34 Analysis

Registrations Hit a New All-Time Weekly High

31,979 new registrations breaks the prior weekly record of 29,577 set in Week 27, and marks the first time weekly registrations have crossed 30,000 since tracking began in Week 5. The jump reverses three consecutive weeks of decline (W31 through W33) in a single move, more than tripling the prior week’s total. Unique owners grew in near-lockstep, up 184.2% to 27,033, keeping the agents-per-owner ratio close to the broad-based pattern seen since Week 32 rather than signaling a concentrated fleet registration event.

Median Trust Score Reaches Its Best Level in Eight Weeks

Median trust score rose to 563, up 19.0% from Week 33’s 473 and the highest weekly figure since Week 26’s 644. Unlike prior volume surges – Week 31’s spike in registrations came with a median trust score of just 200 – this week’s much larger surge in registrations arrived alongside rising, not falling, trust quality. That combination is unusual in this dataset: past registration surges have typically diluted median trust as opportunistic or lower-quality registrations enter alongside legitimate ones.

Agents-Per-Owner Ticks Up Slightly

At 1.18 agents per owner, Week 34 is a bit higher than Week 33’s 1.09 and Week 32’s 1.06, but still far below Week 31’s 2.54 fleet-registration ratio. The modest uptick suggests a small increase in multi-agent registration behavior alongside the broader wave of new individual owners, rather than a return to concentrated batch registration.

Week-on-Week Comparison: W32 – W34

2026 Weekly Registration History – W28 to W34

Note: Median trust score reflects the median Agent Trust Score of agents registered in that specific week, not the cumulative population median. The trust flag breakdown below covers the full indexed population (419,782 agents) as of Week 34, not just this week’s registrations.

Trust Flag Breakdown: What the Full Agent Population Looks Like

The full indexed agent population now stands at 419,782 agents across eight chains, up from 387,255 last week – an increase of 32,527, closely tracking this week’s own registration volume. Here is the updated trust flag breakdown across that entire population.

Sybil-farm flagged agents fell as a share of the population, from 18.39% to 17.05%, even though the absolute count rose slightly (+359 agents) – a sign that this week’s 32,000-agent surge was disproportionately composed of non-farm registrations, diluting the farm share rather than adding to it. FEEDER_UNKNOWN followed a similar pattern, dropping from 8.11% to 7.82% of the population despite an absolute increase of 1,400 agents. This directional split – rising absolute counts but falling population share for the two largest risk flags – is consistent with a genuine influx of new, more broadly distributed registrations this week rather than another farm-driven event.

The most serious flags remain rare and largely unchanged in absolute terms: 769 agents have an owner funded by a confirmed rug pull operator (up 9 from last week), and 61 agents are controlled directly by a wallet with a confirmed rug pull history (unchanged). Confirmed CEX-funded owners ticked up to 2,801 agents but slipped slightly as a population share to 0.67%. Honeypot-linked flags and the structural multisig-council flags remain at effectively zero or unchanged from last week.

About This Tracker

ChainAware.ai’s weekly ERC-8004 agent tracker monitors new agent registrations on the ERC-8004 Identity Registry, the number of unique owner wallets behind those registrations, and the median Agent Trust Score of each week’s registration cohort. Agent Trust Score is a 0-1000 score combining owner wallet fraud probability, feeder address analysis, farm detection, and criminal record checks – see the full Agent Trust Score methodology for how the model scores agents and their controlling wallets. Data is updated weekly alongside ChainAware’s PancakeSwap/Uniswap rug pull tracker.

The post ERC-8004 Agent Trust Tracker Week 34 2026 – Registrations Triple as 32,000 New Agents Join, Trust Score Hits Highest Level Since Week 26 first appeared on ChainAware.ai.

Data covers ERC-8004 agent registrations and Agent Trust Score assessments. Week 33 = week ending Sunday, August 16, 2026.

Week 33 pulled back on both fronts at once. 10,361 new agents registered – down 33.8% from Week 32’s 15,652. Unique owner wallets fell in step, down 35.9% to 9,511, keeping the agents-per-owner ratio essentially flat at 1.09 – still close to the broad, one-agent-per-wallet pattern that returned in Week 32. Median trust score eased 9.2% to 473, staying within the Provisional tier but giving back some of the prior week’s sharp rebound. This week we’re also updating our full trust flag breakdown across the entire indexed agent population, now standing at 387,255 agents.

Week 33 Key Numbers

Week 33 Analysis

Registrations Cool Off After Two Volatile Weeks

After Week 31’s concentration spike and Week 32’s sharp reversal back to broad-based registration, Week 33 simply cooled off on volume. 10,361 new registrations is the lowest weekly total since Week 25’s 5,948, and continues a declining trend that has now run for three consecutive weeks since Week 30’s local peak of 22,989. Unique owners fell by nearly the same proportion as registrations, which is why the agents-per-owner ratio barely moved – this looks like a broad slowdown in registration activity rather than a shift in who is doing the registering.

Trust Score Gives Back Part of the Week 32 Rebound

Median trust score fell from 521 to 473, a 9.2% decline. The score remains inside the Provisional tier (400-599) for a second consecutive week, but the pullback suggests Week 32’s rebound to 521 was not the start of a sustained recovery – trust scores for new registration cohorts continue to swing considerably from week to week, ranging from as low as 200 (Week 31) to as high as 644 (Week 26) over the past eight weeks alone.

Agents-Per-Owner Stays Near One-to-One

At 1.09 agents per owner, Week 33 continues the broad-based registration pattern that returned in Week 32 (1.06), a sharp contrast with Week 31’s 2.54 ratio that signaled fleet-style batch registration. Two consecutive weeks near a 1:1 ratio suggests the concentrated registration event in Week 31 was an isolated episode rather than the start of a new normal – though with only two data points since the reversal, it is too early to call this a stable baseline.

Week-on-Week Comparison: W31 – W33

2026 Weekly Registration History – W27 to W33

Note: Median trust score reflects the median Agent Trust Score of agents registered in that specific week, not the cumulative population median. The trust flag breakdown below covers the full indexed population (387,255 agents) as of Week 33, not just this week’s registrations.

Trust Flag Breakdown: What the Full Agent Population Looks Like

The full indexed agent population now stands at 387,255 agents across eight chains, up from roughly 377,600 last week. Here is the updated trust flag breakdown across that entire population.

Sybil-farm flagged agents held essentially steady at 18.39% of the population (up marginally from 18.73% last week, as the denominator grew faster than new farm detections) – still by far the largest single flag in the dataset. FEEDER_UNKNOWN ticked up slightly to 8.11%, and ERC6551_TBA-controlled agents grew from 1,942 to 2,156, a proportionally larger jump than most other flags this week, though the category remains a small share of the total population at 0.56%.

The most serious flags remain rare in absolute terms: 760 agents have an owner funded by a confirmed rug pull operator (up 2 from last week), and 61 agents are controlled directly by a wallet with a confirmed rug pull history (unchanged). Confirmed CEX-funded owners – the strongest positive legitimacy signal – crept up to 2,778 agents, still just 0.72% of the population. Honeypot-linked flags and the structural multisig-council flags remain at effectively zero or unchanged from last week.

About This Tracker

ChainAware.ai’s weekly ERC-8004 agent tracker monitors new agent registrations on the ERC-8004 Identity Registry, the number of unique owner wallets behind those registrations, and the median Agent Trust Score of each week’s registration cohort. Agent Trust Score is a 0-1000 score combining owner wallet fraud probability, feeder address analysis, farm detection, and criminal record checks – see the full Agent Trust Score methodology for how the model scores agents and their controlling wallets. Data is updated weekly alongside ChainAware’s PancakeSwap/Uniswap rug pull tracker.

The post ERC-8004 Agent Trust Tracker Week 33 2026 – Registrations and Owners Both Fall as Median Trust Cools From Provisional High first appeared on ChainAware.ai.

Data covers ERC-8004 agent registrations and Agent Trust Score assessments. Week 32 = week ending Sunday, August 9, 2026.

Week 32 reversed both of Week 31’s headline trends at once. 15,652 new agents registered – down 26.8% from Week 31’s 21,378. But unique owner wallets rose 76.3% to 14,834, meaning registrations swung from the most concentrated week on record back to nearly one agent per owner. Median trust score also rebounded sharply, up 160.5% to 521, moving out of Elevated Risk and into the Provisional tier. This week we’re also publishing, for the first time, a full breakdown of trust flags across the entire indexed agent population – including how many agents show signs of Sybil farming or a history tied to confirmed rug pull operators.

Week 32 Key Numbers

Week 32 Analysis

Concentration Unwinds – Ownership Spreads Back Out

Last week’s tracker flagged a sharp concentration event: registrations barely moved but unique owners collapsed, pushing agents-per-owner to 2.54x – the clearest sign yet of batch or fleet-style registration. This week, that pattern fully reversed. Registrations fell 26.8% to 15,652, but unique owners rose 76.3% to 14,834, bringing agents-per-owner back down to about 1.06 – close to one agent per wallet, the signature of broad, independent registration rather than a small number of wallets registering in bulk.

Median Trust Score Rebounds Into Provisional Territory

Median trust score jumped from 200 – the Elevated Risk floor – to 521, a Provisional-tier score and a 160.5% increase. This is the highest weekly median trust score recorded since Week 26’s 644. Combined with the more broad-based ownership pattern this week, the data points toward a genuinely different kind of registration cohort than Week 31’s: more independent wallets, each with somewhat stronger trust signals on average.

Trust Flag Breakdown: What the Full Agent Population Looks Like

Separate from this week’s registration numbers, we’re publishing a snapshot of trust flags across the entire indexed agent population – roughly 377,600 agents in total, covering everything registered since tracking began. Each flag below represents a specific pattern the Agent Trust Score model checks for in an agent’s owner wallet or control structure.

Roughly one in five indexed agents – 18.73% – are flagged as belonging to a Sybil fleet, where a single operator has registered many agents that appear independent on the surface. That’s by far the largest single flag in the dataset, and it lines up with the concentration pattern this tracker has now observed directly in the weekly registration data (as in Week 31, before it reversed this week). Almost 1 in 10 agents – 8.06% – have an owner wallet whose funding source could not be traced, and 5.61% have delegated control away from the registered owner to a secondary address, which can make the real controller harder to identify.

At the other end, only 0.73% of agents carry the strongest positive signal available – confirmed funding from a centralized exchange, which typically means the owner passed that exchange’s own identity checks. The most serious flags are rare in absolute terms but still represent real agents: 758 agents have an owner funded by a confirmed rug pull operator, and 61 agents are controlled directly by a wallet with a confirmed rug pull history. Two flags – agents funded by or controlled by confirmed honeypot creators – sit at effectively zero (8 and 0 agents respectively), and several structural flags around unresolvable or large multisig councils currently show no hits at all across the population.

Week-on-Week Comparison: W30 – W32

2026 Weekly Registration History – W26 to W32

Note: Median trust score reflects the median Agent Trust Score of agents registered in that specific week, not the cumulative population median. The trust flag breakdown above covers the full indexed population (~377,600 agents) as of Week 32, not just this week’s registrations.

About This Tracker

ChainAware.ai’s weekly ERC-8004 agent tracker monitors new agent registrations on the ERC-8004 Identity Registry, the number of unique owner wallets behind those registrations, and the median Agent Trust Score of each week’s registration cohort. Agent Trust Score is a 0-1000 score combining owner wallet fraud probability, feeder address analysis, farm detection, and criminal record checks – see the full Agent Trust Score methodology for how the model scores agents and their controlling wallets. Data is updated weekly alongside ChainAware’s PancakeSwap/Uniswap rug pull tracker.

The post ERC-8004 Agent Trust Tracker Week 32 2026 – Registrations Fall as Median Trust Rebounds, Farm Detection Flags 1 in 5 Agents first appeared on ChainAware.ai.

Data covers ERC-8004 agent registrations and Agent Trust Score assessments. Week 31 = week ending Sunday, August 2, 2026.

Week 31 looked stable at first glance – 21,378 new agents registered, only 7.0% below Week 30’s 22,989. But the number of unique owner wallets behind those registrations collapsed, down 58.7% to just 8,413. The gap between those two numbers tells the real story: registrations concentrated sharply into fewer hands this week. Median trust score also fell further, down 33.1% to 200 – the floor of the Elevated Risk tier, and a value historically associated with farm-flagged registration batches.

Week 31 Key Numbers

Week 31 Analysis

Registrations Held Steady – But Unique Owners Collapsed

Taken on its own, a 7.0% drop in registrations would read as an unremarkable, quiet week. It is the unique owner count that changes the picture entirely: only 8,413 distinct wallets registered agents in Week 31, down from 20,355 in Week 30 – a 58.7% collapse. Registrations fell only slightly while the number of participants fell by more than half, which can only mean one thing: the wallets that did register this week each registered far more agents, on average, than in the prior week.

Agents Per Owner More Than Doubles

Dividing registrations by unique owners gives a rough measure of how concentrated a week’s activity is. In Week 30, that ratio was about 1.13 agents per owner – consistent with most owners registering a single agent, broad-based growth. In Week 31, the same ratio jumped to about 2.54 agents per owner – more than double. This is a marked shift from the pattern described in last week’s tracker, where owner growth closely tracked registration growth. This week, a smaller set of wallets accounts for a similar volume of registrations, which is the signature of batch or fleet-style registration activity rather than broad, independent participation.

Median Trust Hits the Tier Floor

Median trust score fell again, down 33.1% to exactly 200 – the lowest boundary of the Elevated Risk tier, one step above Untrusted. This is not a new value in the tracker’s history: several earlier weeks – W07, W09, W13, W21, and W22 – also recorded a median of exactly 200, and those weeks were consistent with cohorts dominated by low-history or farm-flagged registrations rather than organically diverse agent deployments. Combined with this week’s collapse in unique owners, Week 31 fits that same pattern: a smaller number of wallets registering agents in bulk, with a trust profile that reflects it.

Three Weeks, Three Different Stories

The past three weeks illustrate how differently a similar registration number can behave under the surface. Week 29 saw registrations fall sharply alongside owners, in step with each other. Week 30 saw registrations more than double alongside a nearly equal rise in unique owners – broad-based growth with declining trust. Week 31 saw registrations hold roughly steady while unique owners collapsed – concentrated growth with trust at the tier floor. The registration count alone would not have distinguished any of these three weeks from one another; the unique owner metric is what reveals the difference.

Week-on-Week Comparison: W29 – W31

2026 Weekly Registration History – W25 to W31

Note: Median trust score reflects the median Agent Trust Score of agents registered in that specific week, not the cumulative population median. Unique owner wallets counts distinct wallets that registered at least one agent that week; agents per owner is registrations divided by unique owners for that week.

About This Tracker

ChainAware.ai’s weekly ERC-8004 agent tracker monitors new agent registrations on the ERC-8004 Identity Registry, the number of unique owner wallets behind those registrations, and the median Agent Trust Score of each week’s registration cohort. Agent Trust Score is a 0-1000 score combining owner wallet fraud probability, feeder address analysis, farm detection, and criminal record checks – see the full Agent Trust Score methodology for how the model scores agents and their controlling wallets. Data is updated weekly alongside ChainAware’s PancakeSwap/Uniswap rug pull tracker.

The post ERC-8004 Agent Trust Tracker Week 31 2026 – Unique Owners Collapse 58.7% as Registrations Concentrate and Trust Hits Floor first appeared on ChainAware.ai.

Data covers ERC-8004 agent registrations and Agent Trust Score assessments. Week 30 = week ending Sunday, July 26, 2026.

Week 30 saw new ERC-8004 agent registrations more than double. 22,989 new agents registered – up 108.5% from Week 29’s 11,026, the second-highest weekly registration count since tracking began. Unique owner wallets grew at nearly the same pace, up 96.9% to 20,355. But the median trust score of this week’s cohort fell sharply, down 26.4% to 299 – the lowest median trust score recorded since Week 20, and low enough to drop out of the Provisional tier into Elevated Risk.

Week 30 Key Numbers

Week 30 Analysis

Registrations More Than Double

22,989 new agents registered in Week 30, more than double Week 29’s 11,026. This is the second-highest weekly registration count recorded since tracking began, behind only Week 27’s 29,577. Registration volume has now swung sharply for three straight weeks: down 54.9% in Week 29, then up 108.5% in Week 30 – a reminder that weekly registration counts remain highly volatile rather than settling into a stable trend.

Introducing Unique Owners: How Spread Out Is This Week’s Growth?

Starting this week, the tracker also reports unique owner wallets – the number of distinct wallets that registered at least one agent that week. This matters because a large registration number can mean two very different things: it could reflect many different people or teams each registering an agent, or it could reflect a small number of wallets registering large batches of agents at once.

In Week 30, 20,355 unique wallets registered 22,989 agents – an average of about 1.13 agents per owner. In other words, most owners this week registered only a single agent, rather than large batches. Owner wallet growth (+96.9%) closely tracked registration growth (+108.5%), which means the doubling in registrations came from a broad increase in the number of distinct wallets participating, not from a small number of wallets registering many agents each. This is a meaningfully different picture than a scenario where the doubling was driven by one or two operators mass-registering fleets.

Median Trust Score Falls to Its Lowest Since Week 20

Despite registrations being broad-based rather than concentrated in a few wallets, the median trust score of Week 30’s cohort still fell sharply, down 26.4% to 299 – moving out of the Provisional tier (400-599) that the past three weeks had occupied, and into the Elevated Risk tier (200-399). This is the lowest median trust score recorded since Week 20’s 291, and one of the lower readings in the tracker’s history overall. A broad base of new owner wallets does not automatically mean a trustworthy one – many of this week’s registering wallets appear to be new or thin on established on-chain history, which is one of the signals the Agent Trust Score model weighs when assigning a score.

Week-on-Week Comparison: W28 – W30

2026 Weekly Registration History – W24 to W30

Note: Median trust score reflects the median Agent Trust Score of agents registered in that specific week, not the cumulative population median. Unique owner wallets counts distinct wallets that registered at least one agent that week.

About This Tracker

ChainAware.ai’s weekly ERC-8004 agent tracker monitors new agent registrations on the ERC-8004 Identity Registry, the number of unique owner wallets behind those registrations, and the median Agent Trust Score of each week’s registration cohort. Agent Trust Score is a 0-1000 score combining owner wallet fraud probability, feeder address analysis, farm detection, and criminal record checks – see the full Agent Trust Score methodology for how the model scores agents and their controlling wallets. Data is updated weekly alongside ChainAware’s PancakeSwap/Uniswap rug pull tracker.

The post ERC-8004 Agent Trust Tracker Week 30 2026 – Registrations More Than Double to 22,989 as Median Trust Falls to Elevated Risk first appeared on ChainAware.ai.

Data covers ERC-8004 agent registrations and Agent Trust Score assessments. Week 29 = week ending Sunday, July 19, 2026.

Week 29 brought a sharp pullback in new ERC-8004 agent registrations after two unusually active weeks. 11,026 new agents registered – a 54.9% decline from Week 28’s 24,458 and the lowest weekly registration count since Week 24. The median trust score of this week’s registration cohort also fell, down 12.5% to 406, placing the typical new agent squarely in the Provisional tier. ChainAware now has 317,589 agents indexed across the ERC-8004 Identity Registry.

Week 29 Key Numbers

Week 29 Analysis

A Sharp Reversion After Two Elevated Weeks

Weeks 27 and 28 were the two highest registration weeks since tracking began – 29,577 and 24,458 new agents respectively, well above the roughly 6,000-19,000 range that characterized most weeks from W16 through W26. Week 29’s 11,026 registrations mark a return closer to that longer-run baseline rather than a continuation of the W27-W28 surge. Whether that surge reflected a genuine wave of new agent deployment or a temporary registration campaign by a smaller number of operators is not yet clear from registration counts alone – the trust score data below offers a partial answer.

Median Trust Score Also Declined

Unlike a simple reversion where volume falls but quality holds steady, Week 29’s median trust score of 406 is also down from 464 in Week 28 and 432 in Week 27 – a 12.5% week-on-week decline. All three of the most recent weeks sit in the Provisional tier (400-599), but Week 29 is the weakest of the three. This differs from Week 26, where median trust reached 644 (Trusted tier) alongside a comparatively modest 13,816 registrations – the highest median trust score recorded since tracking began.

The historical range for median weekly trust is wide: from a low of 105 in Week 8 up to 644 in Week 26. Several weeks – W07, W09, W13, W21, and W22 – recorded a median of exactly 200, the floor of the Elevated Risk tier, which is consistent with cohorts dominated by low-history or farm-flagged registrations rather than organically diverse agent deployments.

Most Agents Score Either Very Low or Very High

Weekly registration counts and median scores describe the flow of new agents. A separate, and arguably more important, picture is how trust scores are spread across all 317,589 agents currently indexed. In a healthy, evenly-distributed population, most agents would land somewhere in the middle of the scale. That is not what the data shows. Instead, agents tend to fall into one of two groups: a large cluster with very low scores, and a large cluster with very high scores – with comparatively few agents sitting in between.

On the low end, 27.5% of all indexed agents fall into the Untrusted tier – and within that group, 70,435 agents (22.2% of the entire population) score below 100, the single largest group in the whole dataset. These are typically agents flagged for suspicious patterns: fleets of agents registered together, or agents whose controlling wallet has a history of fraud.

On the high end, 21.3% of agents score in the Sovereign tier – the highest trust level ChainAware assigns – with the very top scoring band (900-1000) alone holding 42,749 agents (13.5%), the second-largest group in the dataset. These are typically agents backed by an owner wallet with a long, clean, well-established on-chain history.

In between these two extremes, the three middle tiers – Elevated Risk, Provisional, and Trusted – are each individually smaller than either the bottom or the top group, even though together they cover a much wider range of scores. In other words: it is more common for an agent to be clearly low-trust or clearly high-trust than to be somewhere in the middle.

This pattern lines up with how ChainAware’s Agent Trust Score is designed to work. Several of the checks behind the score apply sharp penalties rather than gradual ones: agents registered in large batches at the same time are flagged as likely fleets and pushed toward the bottom of the scale, and a confirmed history of fraud in an agent’s ownership chain caps the score outright, regardless of anything else. On the other end, an agent backed by an owner with an established, trustworthy track record is lifted quickly toward the top of the scale. Few agents end up stuck in the middle, because the scoring logic is built to sort agents into clear categories rather than spread them out evenly. For the full scoring methodology behind these tier boundaries, see the Agent Trust Score methodology page.

Week-on-Week Comparison: W27 – W29

2026 Weekly Registration History – W23 to W29

Note: Median trust score reflects the median Agent Trust Score of agents registered in that specific week, not the cumulative population median. Total agents indexed (317,589) reflects the full registry snapshot as of Week 29.

About This Tracker

ChainAware.ai’s weekly ERC-8004 agent tracker monitors new agent registrations on the ERC-8004 Identity Registry and the median Agent Trust Score of each week’s registration cohort. Agent Trust Score is a 0-1000 score combining owner wallet fraud probability, feeder address analysis, farm detection, and criminal record checks – see the full Agent Trust Score methodology for how the model scores agents and their controlling wallets. Data is updated weekly alongside ChainAware’s PancakeSwap/Uniswap rug pull tracker.

The post ERC-8004 Agent Trust Tracker Week 29 2026 – Registrations Fall 54.9% as Most Agents Score Either Very Low or Very High first appeared on ChainAware.ai.

ChainAware has indexed 274,792 ERC-8004 agents across Ethereum, BSC, Base, and Avalanche. The data reveals a striking picture of the current agent ecosystem: 26% of indexed agents score Untrusted, 21.1% are flagged as farm-detected Sybil operations, and only 21.1% reach the Sovereign tier. More than half of all registered ERC-8004 agents carry material trust risk — and until today, no infrastructure existed to surface that risk before an interaction.

The Problem Agent Trust Score Solves

Agentic commerce — where AI agents execute transactions autonomously on behalf of users — is accelerating rapidly. McKinsey estimates AI agents could mediate $3-5 trillion in global commerce by 2030 . Today, 68% of new DeFi protocols launched in Q1 2026 include at least one autonomous AI agent. Every one of those agents initiates transactions without human approval — and every one of those transactions is a trust decision that previously had no infrastructure layer behind it.

The ERC-8004 Identity Registry tells you an agent exists. It does not tell you whether to trust it. Voting-based reputation systems can be gamed in hours — an operator deploying 50 agent wallets can manufacture a full review history at near-zero cost. ChainAware’s Agent Trust Score bypasses the reputation layer entirely and scores the human controlling the agent: their on-chain behavioral history, who funded their wallet, and whether they have previously created rug pull pools or honeypot tokens.

What the Data Shows

Additional signals: 21.1% carry the FARM_DETECTED flag, 9.5% have unknown feeder addresses, 7.6% use EIP-7702 delegated ownership, and 741 agents have confirmed rug pull history in their feeder chain.

CB Insights Validation

The launch follows ChainAware’s inclusion in the CB Insights AI Fraud Prevention Market Map , placing ChainAware in the On-Chain Intelligence category alongside Chainalysis, Elliptic, and TRM Labs. Read the full analysis in our CB Insights market map coverage.

Co-Founder Statement

“We indexed 274,792 ERC-8004 agents and found that more than half score either Untrusted or Elevated Risk. The agentic economy is being built on top of a registry that has no trust infrastructure. Agent Trust Score is the infrastructure that closes that gap.” — Martin Ploom, Co-Founder, ChainAware.ai

Integration

The Agent Trust Score API returns a 0-1000 score, tier, and flag set for any indexed ERC-8004 agent in under 100ms. It integrates natively with ChainAware’s Prediction MCP server. Enterprise rate limits, SLA, and webhook notifications are available on request.

For the full technical breakdown, integration guide, and ERC-8004 ecosystem data analysis, read the deep-dive: Agent Trust Score: On-Chain Trust Scoring for the Agentic Commerce Era →

ChainAware.ai is the Web3 Agentic Growth Infrastructure — 20M+ wallet personas, 98% fraud detection accuracy, <100ms API latency. Named in CB Insights’ AI Fraud Prevention Market Map. chainaware.ai

The post ChainAware Launches Agent Trust Score — On-Chain Trust Scoring for the Agentic Commerce Era first appeared on ChainAware.ai.

Something fundamental is changing in how commerce works. AI agents — software systems that can perceive, decide, and act autonomously — are beginning to transact. They are paying for APIs, settling invoices, executing DeFi strategies, managing DAO treasuries, and interacting with financial infrastructure in ways that traditional systems were never designed to handle. McKinsey estimates AI agents could mediate $3-5 trillion in global commerce by 2030 . Today, 68% of new DeFi protocols launched in Q1 2026 already include at least one autonomous AI agent.

Every one of those agents initiates transactions without human approval. Furthermore, every one of those transactions is a trust decision — a question of whether the agent on the other side of the interaction is controlled by a legitimate operator, or by someone whose on-chain history includes rug pulls, honeypot tokens, mixer exposure, and Sybil farming at scale. Until today, no infrastructure existed to answer that question. ChainAware’s Agent Trust Score is that infrastructure.

Read the launch announcement: ChainAware Launches Agent Trust Score — Official Announcement →

Table of Contents

1. The Agentic Commerce Era: Why Trust Is the Missing Layer
2. The Agentic Commerce Trust Stack: Where Agent Trust Score Fits
3. CB Insights Validation: ChainAware in the AI Fraud Prevention Market Map
4. What ERC-8004 Gives You — and What It Doesn’t
5. State of the ERC-8004 Registry: Trust Analysis of 274,792 Agents
6. The Five Signals Only ChainAware Provides
7. The Data Moat: Why This Cannot Be Replicated
8. How the Agent Trust Score Works
9. Score Tiers: What Each One Means
10. The Compounding Risk of Unscreened Agent Access
11. Integration Guide for DeFi Protocol Builders
12. Guide for Agent Creators: How Your Score Is Determined
13. How Agent Trust Score Compares to Other Platforms
14. Frequently Asked Questions

The Agentic Commerce Era: Why Trust Is the Missing Layer

Agentic commerce is not a future scenario — it is happening now, across every DeFi protocol that accepts agent-initiated transactions. Consequently, DeFi protocol builders face an immediate and urgent problem: how do you trust an agent you have never met, whose controlling wallet was created last week, whose funding source you cannot trace, and whose operator may have a history of financial fraud under a different wallet identity?

The scale of the shift is concrete. Morgan Stanley projects that nearly half of all online shoppers will use AI shopping agents by 2030 , accounting for approximately 25% of their total spending. In Web3 specifically, the transition is even faster — agents are moving from advisory roles (suggesting trades) to execution roles (completing them). The distinction between advice and execution is the distinction between a bad recommendation and an empty wallet.

Three converging forces are accelerating this shift in Web3. First, ERC-8004 went live on Ethereum mainnet in January 2026, giving AI agents a standardized on-chain identity for the first time. Second, x402 — the open payment protocol championed by Coinbase, Google Cloud, and Circle — provides agents with stablecoin-native payment rails, enabling micropayments without human login flows. Third, trust infrastructure has lagged behind. As Google Cloud’s Global Head of Strategy for Web3 stated at Consensus 2026: “The biggest friction points center on the fact that most products are still built for humans, not agents.” Agent Trust Score is ChainAware’s response to that friction at the trust layer specifically.

For a deep dive into the commercial context, see our article on why the first step in agentic commerce is trust, not integration.

The Know Your Agent (KYA) Imperative

Know Your Agent — KYA — is emerging as the agent-layer equivalent of KYC. Unlike KYC, however, KYA for Web3 is necessarily on-chain behavioral rather than documentary. There are no passports in DeFi. Instead, there is transaction history — permanent, public, immutable, and available for scoring without touching any personal data. KYA answers the same fundamental question KYC does — who is this entity, should I trust them? — using behavioral pattern analysis across 20M+ wallet personas trained on confirmed fraud and legitimate address populations.

The regulatory tailwind is real. The EU AI Act , which takes full effect in August 2026, creates documentation and risk assessment requirements for high-risk AI systems. Autonomous agents with financial execution permissions are a clear candidate for high-risk classification. Protocols operating in EU-regulated markets need demonstrable risk controls for agent interactions — Agent Trust Score satisfies that requirement without adding friction for legitimate agents.

The Agentic Commerce Trust Stack: Where Agent Trust Score Fits

The agentic economy has built four of its five required infrastructure layers. The fifth — trust — launched today. Understanding where Agent Trust Score sits in the full stack clarifies both what it does and why no existing protocol addresses the same problem.

This framework — which we call the Agentic Commerce Trust Stack — maps the four distinct problems any agentic commerce protocol must solve before autonomous transactions can happen safely. Each layer answers a different question. Moreover, each layer requires different infrastructure. x402 handles payment rails. ERC-8183 handles disputes after transactions complete. ERC-8004 handles identity registration. Agent Trust Score handles the question that must be answered before any interaction begins: is this agent controlled by someone whose on-chain history warrants the trust implied by autonomous execution?

The stack is not theoretical. Mastercard completed Europe’s first live AI-agent bank payment in 2026. Visa launched an agent-focused payment framework. TON Foundation launched Agentic Wallets in April 2026, allowing AI agents on Telegram to autonomously store and spend funds within user-defined limits. The payment rails are live. The dispute layer is being built. The identity layer has 274,792 registered agents. The trust layer — until today — was the missing piece.

CB Insights Validation: ChainAware in the AI Fraud Prevention Market Map

Context matters for a product launch in a nascent category. ChainAware’s inclusion in the CB Insights AI Fraud Prevention Market Map provides that context clearly. CB Insights mapped 200+ of the most promising companies building AI-powered fraud prevention infrastructure, from deepfake detection to on-chain intelligence and — their exact words — “agentic trust infrastructure.” ChainAware appears in the On-Chain Intelligence category alongside Chainalysis, Elliptic, and TRM Labs.

The CB Insights placement is significant for three reasons. First, it validates the methodology — CB Insights uses Mosaic scores and predictive signals, not self-reported data, to select companies. Second, it identifies “agentic trust infrastructure” as a standalone market segment within fraud prevention — confirming that Agent Trust Score addresses a recognized institutional need, not a speculative niche. Third, it positions ChainAware as the Web3-native player in a category otherwise dominated by forensic analytics firms whose methodology is reactive rather than predictive.

For the full analysis of what the CB Insights placement means for ChainAware’s market position, see our CB Insights market map coverage article. For the competitive landscape of agent trust platforms specifically, see our Agent Trust Infrastructure Race analysis comparing six platforms across 19 capabilities.

What ERC-8004 Gives You — and What It Doesn’t

ERC-8004 is a well-designed standard that solves a specific problem: giving AI agents a standardized, verifiable on-chain identity. Every registered agent receives an ERC-721 NFT representing its identity, a controlling owner wallet, an agent payment wallet, and a URI pointing to its agent card JSON. The registry answers the question “does this agent exist?” cleanly and cryptographically.

The standard does not answer the question “should I trust this agent?” — and the specification explicitly leaves scoring to third parties. This design choice is correct architecturally: the registry should be a neutral data layer, not an opinion engine. However, it means that every protocol integrating ERC-8004 agents is responsible for answering the trust question independently. Most currently do not — they query the registry to confirm the agent exists and proceed. Agent Trust Score is what fills the gap between “agent exists” and “agent is safe to interact with autonomously.”

The Voting-Based Reputation Problem

ERC-8004 also includes a built-in Reputation Registry — a standard interface for peer feedback. On paper, this sounds like a trust mechanism. In practice, it is a manufactured-trust system waiting to be exploited. An operator deploying 50 agent wallets can have each one review every other, generating a full positive reputation history in hours at a cost measured in gas fees. On BSC or Base, that cost is less than a dollar. The result is indistinguishable from genuine reputation on any platform that reads the registry naively.

ChainAware does not read the ERC-8004 Reputation Registry to compute the Agent Trust Score. Instead, we look behind the agent at the behavioral history of the wallets controlling it and funding its controller. That history is immutable — it cannot be manufactured overnight. An operator who rugged three liquidity pools in Q4 2025 and registered 47 agents in Q1 2026 carries that history forward regardless of how many peer reviews the new agents accumulate. For a detailed analysis of why voting-based reputation fails at scale, see our guide to Web3 Wallet Auditing Providers in 2026.

State of the ERC-8004 Registry: First Ever Trust Analysis of 274,792 Agents

ChainAware has indexed the complete ERC-8004 registry — 274,792 agents across Ethereum mainnet, BSC, Base, and Avalanche — and applied the Agent Trust Score to produce the first comprehensive behavioral trust analysis of the agentic economy. The results reveal a striking picture that should concern any DeFi protocol granting autonomous execution access to registry agents.

Trust Score Distribution

The headline finding is stark: more than half of all ERC-8004 registered agents — 51.8% — carry Elevated Risk or Untrusted scores. This is not a marginal tail of bad actors. It is the majority of the registry. Protocols granting autonomous execution access to unscreened ERC-8004 agents are, statistically, granting that access to a population where more than half present material trust risk.

Flag Analysis: What Is Driving Low Scores

• FARM_DETECTED: 58,061 agents (21.1%) — one in five agents belongs to a Sybil fleet where a single operator controls multiple agents
• FEEDER_UNKNOWN: 26,124 agents (9.5%) — nearly 1 in 10 agents has an owner wallet with an obfuscated or untraceable funding source
• EIP7702_DELEGATED: 20,946 agents (7.6%) — the registered owner has delegated control to a secondary address, potentially obscuring the real controller
• FEEDER_CEX_VERIFIED: 2,691 agents (1.0%) — confirmed CEX-funded owners, the strongest legitimacy signal available
• FEEDER_RUG_HISTORY: 741 agents (0.3%) — agents whose owner wallet was funded by a confirmed rug pull operator
• CREATOR_RUG_HISTORY: 59 agents (0.02%) — agents controlled directly by confirmed rug pull creators
• CREATOR_HONEYPOT_HISTORY: 3 agents — agents whose owner has previously created honeypot token contracts

The farm detection finding deserves particular attention. 58,061 agents — 21.1% of the entire registry — are controlled by fleet operators running multiple agents simultaneously, frequently registered in the same block. Individual agent scoring is structurally blind to this pattern. ChainAware detects it because we maintain an owner profile database tracking fleet size across all indexed chains — a capability that requires the full registry index, not just individual agent lookups.

For the full competitive context of how these signals compare to what RNWY, SkyeProfile, AXIS T-Score, and DJD Agent Score provide, see our Agent Trust Infrastructure Race analysis.

The Five Signals Only ChainAware Provides

Agent Trust Score combines signals that no other agent trust platform currently provides. Each one addresses a specific threat model that the other approaches structurally cannot reach. We intentionally do not publish the exact weights, thresholds, or model coefficients behind these signals — doing so would allow bad actors to calibrate their behavior to stay just below each detection threshold. What we do publish are the signal categories and what each one means for your trust decision.

Signal 1: Owner Wallet Behavioral Fraud Score

The owner wallet is the human or entity controlling the agent. ChainAware scores it using a predictive AI model trained on 20M+ wallet personas across Ethereum, BSC, Base, and beyond — achieving 98% fraud detection accuracy. This is not a blacklist check. Rather, it is a forward-looking behavioral prediction: given this wallet’s complete transaction history, what is the probability it will engage in fraudulent activity? The model retrains continuously on new confirmed fraud cases, meaning evasion strategies become stale quickly.

The fraud score is the primary input to the Agent Trust Score — a clean wallet starts at a high baseline, while a fraud-flagged wallet scores low regardless of any other signal. For the full methodology behind the fraud prediction model, see our AI-Powered Blockchain Analysis guide.

Signal 2: Feeder Address Analysis

The feeder address is the wallet that funded the owner. No other agent trust platform traces and scores this signal. ChainAware traces feeder addresses for approximately 38% of indexed agents. The feeder signal has three variants that carry different trust implications. A CEX-verified feeder (Binance, Coinbase, Kraken, OKX withdrawal address) implies the owner passed KYC somewhere upstream — ChainAware’s strongest positive signal. An unknown or obfuscated feeder is itself a risk signal. A feeder with confirmed fraud status applies hard suppression to the final score.

When the feeder address has rug pull or honeypot history in ChainAware’s database, the owner may have deliberately cycled wallets to obscure a fraud track record — the feeder criminal record check is what catches this pattern. For more on how feeder analysis works in practice, see our Forensic vs AI Blockchain Analysis guide.

Signal 3: Criminal Record — Rug Pull and Honeypot History

ChainAware maintains a database built from on-chain liquidity pair history and token audit data spanning over a year of activity. This database records which wallet addresses created pools that subsequently exhibited rug pull patterns, and which wallet addresses previously deployed honeypot token contracts. Before computing the Agent Trust Score, ChainAware cross-references both the owner wallet and the feeder address against this database.

Criminal record signals result in hard caps on the Agent Trust Score that no other signal can override. This is the signal that connects yesterday’s token fraud to today’s agent deployment. An operator who rugged pools on PancakeSwap in Q4 2025 and registered 40 agents in Q1 2026 is caught by this check. No other agent trust platform makes that connection because no other platform maintains a paired rug pull database and cross-references it against agent registry data. For background on how rug pull and honeypot patterns are detected, see our guide to Rug Pull vs Pump and Dump in Web3.

Signal 4: Trust Delegation

Agent payment wallets are frequently fresh addresses created specifically for the agent — they have no transaction history, no counterparty network, and no behavioral record. A naive scoring approach penalises every newly deployed agent regardless of the owner’s reputation, producing low scores for legitimate agents and making the score useless as a gate for new deployments.

ChainAware’s trust delegation mechanism solves this: the owner wallet’s reputation sets a floor for the agent wallet’s effective score. A reputable developer deploying their first agent wallet scores well through delegation. A fraud-flagged owner cannot delegate any meaningful trust — the delegation collapses, and the agent score reflects the owner’s history rather than the wallet’s lack of it.

Signal 5: Fleet-Level Farm Detection

Every competitor in this market scores agents individually. ChainAware maintains an owner profile database tracking agent fleet size across all indexed chains. Owners controlling unusually large numbers of agents — particularly those registered in tight time windows — receive farm modifiers that suppress scores across their entire fleet, regardless of how any individual agent scores in isolation.

This fleet-level view catches the specific agentic commerce attack pattern that individual scoring cannot surface: one operator manufacturing ecosystem depth through a controlled population of agents, each of which appears clean when scored independently. Our data shows 58,061 agents — 21.1% of the entire ERC-8004 registry — are already in this category today.

The Data Moat: Why This Cannot Be Replicated

Agent Trust Score is built on three proprietary data assets accumulated over years of continuous operation. A competitor starting today cannot purchase these assets, compress the time required to build them, or replicate them from publicly available sources alone. Each one compounds in value as it grows — making ChainAware’s intelligence advantage wider over time, not narrower.

20M+ Wallet Personas: Years of Behavioral Training Data

ChainAware’s fraud prediction model is trained on more than 20 million wallet personas across 8 blockchains. Each persona represents a complete behavioral fingerprint — transaction history, timing patterns, counterparty networks, protocol diversity, AML exposure, and dozens of derived features. Building this dataset required years of continuous on-chain data collection, labeling of confirmed fraud cases, and iterative model retraining against real-world fraud outcomes. The result is 98% fraud detection accuracy on held-out test data.

This persona depth cannot be replicated quickly. A new entrant would need years of historical blockchain data, a confirmed fraud label dataset from real fraud cases, and the engineering infrastructure to process and update 20 million behavioral profiles continuously. Furthermore, the model improves as the dataset grows — each new confirmed fraud case sharpens the prediction boundary. ChainAware’s advantage in this dimension compounds daily.

One Year of On-Chain Pair History: The Criminal Record Database

The rug pull and honeypot criminal record check that powers Agent Trust Score’s hardest caps requires a database of historical liquidity pair creation and token audit results — accumulated over more than a year of continuous on-chain monitoring. ChainAware has tracked pair creation across PancakeSwap, Uniswap, and other major DEX venues, cross-referencing creator wallet addresses against confirmed fraud outcomes: pools where liquidity was removed in rug pull patterns, and token contracts that embedded honeypot mechanics preventing buyers from selling.

This database is what catches the serial fraudster who registers new agents after previous fraud campaigns. It connects the rug puller of November 2025 to the agent creator of February 2026 — a connection that exists only if you have the historical data linking both events to the same wallet address. No competitor in the agent trust scoring market currently maintains this database. Furthermore, building it retroactively is impossible: the historical pair data exists on-chain, but the labeling of fraud outcomes requires the passage of time to observe liquidity removal patterns after the fact. For the data behind this detection engine, see our Rug Pull Tracker report and our AI-Based Predictive Fraud Detection guide.

38% Feeder Coverage: The Funding Chain Network

ChainAware traces feeder addresses — the wallets that funded owner wallets — for approximately 38% of indexed ERC-8004 agents. This coverage reflects the on-chain reality: some owner wallets receive funds from multiple sources, some from bridge or faucet infrastructure that does not produce a single attributable feeder, and some from deliberately obfuscated multi-hop paths. The 38% with traceable single-hop feeders represents the population where the funding chain reveals meaningful intelligence.

Feeder coverage is itself a compounding asset. As ChainAware indexes more agents and more wallet interactions over time, the feeder network graph grows denser — revealing connections between previously unlinked wallet clusters. An operator who cycled through three funding wallets across different campaigns may appear disconnected today but becomes linkable as the transaction graph accumulates more data points. Additionally, the CEX label database — identifying which addresses are verified exchange hot wallets — improves as exchange infrastructure evolves and new verified addresses are confirmed. The feeder signal is the one no competitor has reached because building it requires both the feeder tracing infrastructure and the fraud intelligence to score what you find at the other end of the funding chain.

Why the Moat Compounds

Each of these three data assets improves as it grows larger and older. More wallet personas means better fraud prediction boundary precision. More pair history means more confirmed criminal records attached to operator wallets. More feeder coverage means more fraud connections surfaced across the registry. A competitor starting today with identical engineering resources would still need years to catch up — and by the time they did, ChainAware’s assets would be proportionally larger still. This is the definition of a compounding data moat: the advantage is not a snapshot, it is a trajectory.

How the Agent Trust Score Works

The Agent Trust Score uses a multi-layer pipeline that combines owner fraud probability, trust delegation, feeder analysis, farm detection, and criminal record hard caps into a single 0-1000 score. Each layer applies a specific transformation before passing the result to the next.

We intentionally do not publish the exact weights, thresholds, or multipliers used in each layer. Publishing precise thresholds would allow bad actors to calibrate their behavior to stay just below each detection cap — which would directly undermine the system’s ability to catch sophisticated fraud. Our scoring model retrains continuously on new confirmed fraud patterns. What works as evasion today becomes detectable tomorrow as the model updates.

What we do publish is what each layer measures and why it matters:

• Owner fraud probability — the primary driver of the base score. A clean owner wallet starts at a high baseline. A fraud-flagged wallet scores low regardless of any other input.
• Experience and on-chain history — a modest bonus for owners with genuine DeFi activity over time. Experience adds to the score but cannot compensate for high fraud probability.
• Trust delegation — lifts a fresh agent wallet when the owner has strong history. Collapses when the owner is fraud-flagged.
• Feeder modifier — adjusts the score based on who funded the owner. CEX-verified feeders boost the score. Unknown or fraud feeders suppress it.
• Farm modifier — suppresses scores across an entire fleet when the owner controls an unusually large number of agents or registers them in bulk.
• Criminal record hard caps — override all other signals when confirmed rug pull or honeypot history is found. These caps are absolute and cannot be offset by positive signals elsewhere.

The result is a score between 0 and 1000. The full methodology overview — covering signal categories without exposing exact parameters — is available at chainaware.ai/learn/agent-trust-score.

Score Tiers: What Each One Means for Protocol Builders

The Agent Trust Score maps to five tiers on the 0-1000 scale. Each tier carries a specific operational recommendation for DeFi protocol builders. The right threshold for your protocol depends on the risk profile of the transactions involved — a high-value lending protocol and a low-value DEX swap use different thresholds against the same score.

Sovereign (800-1000) — Full Autonomous Access

Sovereign agents have strong owner fraud probability, a clean or CEX-verified feeder address, no criminal record signals, and no farm detection flags. Sovereign is appropriate for high-value autonomous operations: large-value lending, treasury management, and governance participation with financial consequences. Protocols can grant Sovereign agents the same execution permissions they would grant to established protocol participants.

Trusted (600-799) — Standard Integration

Trusted agents have strong owner fraud probability, a generally clean feeder, and no hard-cap signals. Trusted is appropriate for standard DeFi integrations — trading agents, yield optimisers, and automated compliance workflows where individual transaction risk is moderate and human monitoring is available as a backstop.

Provisional (400-599) — Monitoring Required

Provisional agents show mixed signals: moderate fraud probability, unknown feeder, or a fresh payment wallet. Provisional agents should not receive unsupervised autonomous execution access for high-value operations. However, they are appropriate for lower-risk automated workflows with active monitoring — read-only queries, low-value token swaps, or agentic onboarding flows where individual transaction size is capped.

Elevated Risk (200-399) — Restricted Access Only

Elevated Risk agents carry weak owner history, obfuscated feeders, or farm detection signals. These agents should not be permitted autonomous financial execution. If your protocol needs to serve Elevated Risk agents — for example in a permissionless DEX context — transaction size limits, velocity caps, and real-time monitoring should all be active simultaneously.

Untrusted (0-199) — Block

Untrusted agents carry active fraud signals, confirmed rug pull or honeypot history, confirmed farm detection, sanctioned address exposure, or repeat offender status. These agents should be blocked at the access control layer before any transaction reaches the execution layer. The score is not borderline — it reflects definitive fraud signals from immutable on-chain history. For context on the types of on-chain fraud that produce Untrusted scores, see our AI-Based Predictive Fraud Detection guide.

The Compounding Risk of Unscreened Agent Access

Human-initiated fraud and agent-initiated fraud differ in one fundamental operational characteristic: velocity. A fraudulent human interacting with your protocol manually can execute perhaps dozens of interactions before detection. A fraudulent agent operating autonomously executes thousands of interactions in the same period — at machine speed, without sleep, without rate-limit awareness unless you specifically implement it, and with the full behavioral sophistication of the AI model powering it.

Therefore, the cost of a single misidentified agent is not comparable to the cost of a single misidentified human user. The exposure scales with the agent’s operational capacity. A lending protocol that grants a fraudulent agent autonomous execution access for six hours faces losses that scale with protocol TVL and agent transaction rate. Traditional fraud detection tools are particularly poorly suited to this environment. Rule-based systems flag agent behavior as suspicious because agents naturally exhibit the patterns those rules target: high velocity, cross-category activity, unusual timing distributions. Consequently, you end up blocking legitimate agents while missing sophisticated fraudulent ones engineered to mimic human behavioral patterns.

The Farm Attack at Scale

Agent farming is a specific attack pattern that compounds differently from individual fraud. Consider the operational math: one operator registers a large fleet of agents across BSC and Base, each appearing clean individually. Each agent interacts with your protocol at modest frequency. Collectively, that generates thousands of agent interactions per day from a single coordinated operator. Furthermore, because each agent appears to be an independent participant, your protocol’s per-user rate limits and monitoring thresholds are never triggered on any single agent. Across a week, you may process tens of thousands of transactions from what is effectively a single fraud operation — without any individual agent exceeding your anomaly detection thresholds.

ChainAware’s fleet-level farm detection catches this pattern before the first transaction. When agents from the same owner wallet query the Agent Trust Score API, they return FARM_DETECTED — regardless of how clean any individual agent appears. The trust decision happens at the fleet level, not the individual agent level, because the fraud pattern exists at the fleet level. Our data shows 58,061 agents — 21.1% of the entire ERC-8004 registry — already carry the FARM_DETECTED flag. For the broader context of how predictive models compare to forensic analytics for this class of threat, see our Forensic vs AI analysis guide.

The Serial Fraudster Rotation Pattern

A second compounding risk pattern is the serial fraudster who rotates wallet identities between campaigns. The typical sequence: Wallet A runs a rug pull campaign, extracts funds, and becomes known to forensic databases. Wallet B is then created fresh, funded from Wallet A — the feeder relationship recorded immutably on-chain — and used to register new agents on ERC-8004. Every platform that scores only the agent or the current owner wallet sees a clean Wallet B. ChainAware traces the feeder and scores Wallet A, which carries the rug pull history. Wallet B’s agents receive suppressed scores regardless of how clean Wallet B’s own transaction history appears.

The data confirms this pattern is live in the current registry: 741 agents carry the FEEDER_RUG_HISTORY flag, meaning their owner wallet was funded by a confirmed rug pull operator. These 741 agents appear completely clean to any platform that does not trace the feeder chain. They represent confirmed serial fraudsters using fresh wallets to continue operations under new identities. For the macro picture of rug pull losses, see our Rug Pull Tracker report.

Integration Guide for DeFi Protocol Builders

Adding Agent Trust Score to a DeFi protocol requires one additional step between the ERC-8004 registry lookup and transaction execution. That step takes under 100ms and returns a structured output the protocol’s access control layer can act on directly.

The Trust-Aware Integration Pattern

Agent initiates transaction
  ↓
Resolve agent_id → owner_address + agent_wallet (ERC-8004 registry)
  ↓
GET /erc8004/agent/{chain_id}/{agent_id}/trust-score
  ↓
Response:
{
  "agent_trust_score": 882,
  "tier": "Sovereign",
  "flags": ["FEEDER_CEX_VERIFIED"],
  "scored_at": "2026-07-12T09:14:00Z"
}
  ↓
score ≥ protocol_threshold → execute
score < protocol_threshold → reject or route to human review

The threshold is your decision. Different use cases warrant different risk tolerances:

MCP Integration

Agent Trust Score integrates natively with ChainAware’s Prediction MCP server. Any Claude-based DeFi agent can call agent trust scoring as a native tool call without custom API integration code. For teams building on the MCP stack, our library of 32 ready-made agents includes agent verification logic that can be cloned and deployed in under 30 minutes. For DeFi credit scoring alongside agent trust verification, see our DeFi Credit Score Platform Comparison.

Latency and Rate Limits

Agent Trust Score returns results in under 100ms for pre-indexed agents. ChainAware pre-indexes the full ERC-8004 registry continuously, so the vast majority of queries return cached scores without a live computation cycle. Enterprise plans include dedicated rate limits, SLA guarantees, webhook notifications for score changes, and a dedicated integration engineer. Free tier covers the first 1,000 queries per month with no API key required for public indexed agents. For the AML and MiCA compliance context, see our DeFi Compliance and KYT/AML guide.

Guide for Agent Creators: How Your Score Is Determined

If you are deploying ERC-8004 agents, understanding how your agents are scored enables you to optimize for legitimate trust signals. The score reflects real behavioral history — it cannot be manufactured, but it can be legitimately improved over time through genuine on-chain activity.

What Improves Your Score

The primary lever is your owner wallet’s fraud probability — keeping it low requires genuine, diverse on-chain activity over time. Specifically, interacting with a range of DeFi protocols (lending, trading, staking, bridging) across multiple months produces a behavioral profile that scores well. Additionally, using a CEX withdrawal as your owner wallet’s funding source (Binance, Coinbase, Kraken) immediately flags your agent as FEEDER_CEX_VERIFIED and applies the maximum feeder boost. Furthermore, deploying agents with consistent patterns over time rather than in bulk avoids farm detection signals.

What Permanently Caps Your Score

Criminal record signals are immutable. A confirmed rug pull or honeypot in your on-chain history permanently caps your agents — regardless of how clean your current behavior is. These caps exist because the on-chain events that trigger them are permanent. A wallet that drained a liquidity pool cannot remove that event from the blockchain. Consequently, there is no path to a high Agent Trust Score for operators with confirmed fraud history. For context on how rug pull and honeypot patterns are detected, see our Rug Pull Detector learn page.

How Agent Trust Score Compares to Other Platforms

Agent trust scoring is a new market with several emerging approaches. Each platform answers a different question about the same agent. Understanding the distinction matters for protocol builders choosing a trust gating system — selecting the wrong approach means the specific fraud pattern you face is precisely the one your chosen platform cannot detect.

RNWY is the most established competitor and provides strong review quality analysis and sybil detection. However, their core methodology solves fake reviews — not fake owners. ChainAware solves fake owners. These are complementary approaches: DeFi protocols can use both simultaneously, with RNWY for reputation display and ChainAware as the fraud intelligence gate before execution. For the full 19-capability comparison across all six platforms, see our Agent Trust Infrastructure Race.

Frequently Asked Questions

Which chains does Agent Trust Score cover?

Agent Trust Score indexes ERC-8004 agents across Ethereum mainnet, BSC (BNB Chain), Base, and Avalanche C-Chain, with Mantle in progress. The owner wallet and feeder scoring draws on ChainAware’s broader behavioral intelligence database, which covers 8 blockchains including Polygon, TON, TRON, and HAQQ. Chain coverage expands continuously as new ERC-8004 registry deployments go live.

Does it require any personal data or KYC?

No. Agent Trust Score is derived entirely from public on-chain data. No personal information is collected, no identity verification is required, and no data is stored beyond what is already publicly available on the blockchain. The product is compatible with DeFi’s privacy-first ethos and compliant with GDPR by design.

Can an agent improve its score over time?

Yes — through the owner wallet’s behavioral history, not through the agent wallet itself. As the owner wallet accumulates genuine on-chain experience and maintains a clean fraud probability score, the Agent Trust Score improves. However, criminal record signals are permanent — they do not improve over time because the underlying on-chain events are immutable.

Why don’t you publish the exact scoring weights?

Publishing exact thresholds and weights would allow bad actors to calibrate their behavior to stay just below each detection cap. This is the same reason credit scoring agencies (FICO, Experian) publish the signal categories they use — payment history, credit utilization, account age — without publishing the precise weights. Our fraud model retrains continuously, so even partial knowledge of current parameters becomes stale quickly. The signal categories are published at chainaware.ai/learn/agent-trust-score. The exact parameters remain proprietary.

What happens when an agent is transferred to a new owner?

ERC-8004 agents are ERC-721 NFTs and can be transferred between wallets. When ChainAware detects an ownership transfer, the Agent Trust Score recalculates using the new owner wallet’s behavioral history. The score tracks the current controlling entity, not the original registrant. An agent cannot inherit a previous owner’s strong score after transfer.

How does EIP-7702 delegation affect the score?

When EIP-7702 delegation is detected, ChainAware scores both the registered owner and the delegate address. The Agent Trust Score takes the less favorable of the two results. Agents with EIP-7702 delegation are flagged explicitly in the API response as EIP7702_DELEGATED, giving protocol builders the option to apply additional scrutiny regardless of the final numerical score. Currently 7.6% of indexed agents — 20,946 agents — use EIP-7702 delegation.

How is Agent Trust Score different from Wallet Reputation Score?

Both use the same 0-1000 scale, making them directly comparable. However, Agent Trust Score applies the scoring to multiple addresses simultaneously — owner wallet, agent wallet, and feeder address — and combines them using trust delegation logic and fleet-level farm detection signals that do not exist in the standalone Wallet Reputation Score. Additionally, Agent Trust Score cross-references the criminal record database for rug pull and honeypot history. For the Wallet Reputation Score methodology, see our Wallet Auditor learn page.

What is the free tier?

The free tier covers 1,000 queries per month for indexed public agents on Ethereum, BSC, Base, and Avalanche. No API key required to start — simply query beta.chainaware.ai/agent-trust-score with any agent ID, owner address, or agent wallet. Enterprise plans with higher rate limits, SLA, webhooks, and dedicated integration support are available via chainaware.ai/schedule.

Further Reading

• Agent Trust Score — Methodology Overview
• The Agent Trust Infrastructure Race: Six Platforms Compared
• The First Step in Agentic Commerce Isn’t Integration. It’s Trust.
• CB Insights AI Fraud Prevention Market Map — ChainAware Coverage
• AI-Powered Blockchain Analysis for Crypto Security 2026
• Rug Pull Tracker — $569M on PancakeSwap V2
• DeFi Compliance: Complete KYT and AML Guide 2026
• Web3 Wallet Auditing Providers in 2026 — Complete Comparison
• Forensic vs AI-Powered Blockchain Analysis
• DeFi Credit Score Platforms Compared 2026
• Prediction MCP Setup Guide
• 32 Ready-Made Agents

ChainAware.ai is the Web3 Agentic Growth Infrastructure — behavioral intelligence for DeFi protocols, AI agents, and individual crypto users. 20M+ wallet personas, 98% fraud detection accuracy, <100ms API latency across 8 blockchains. Named in CB Insights’ AI Fraud Prevention Market Map in the On-Chain Intelligence category. chainaware.ai

The post ChainAware Launches Agent Trust Score – On-Chain Trust Scoring for the Agentic Commerce Era first appeared on ChainAware.ai.

A $3-5 trillion market is forming around one unsolved problem: how do you know whether to trust an AI agent before it touches your funds? Six distinct approaches have emerged in 2026 to answer that question. They carry similar names – trust scores, reputation scores, behavioral scores – but they answer fundamentally different questions, protect against different threat models, and leave very different blind spots.

Choosing the wrong approach does not mean you get a slightly worse score. It means the specific fraud pattern you face is exactly the one your chosen platform cannot detect. An operator running a Sybil farm of 50 agents will not be caught by a review-quality platform scoring each agent individually. A serial rug puller launching agents under a fresh wallet will not be caught by a platform that scores wallet age but ignores creation history. Understanding which approach catches which threat is the most important infrastructure decision in agentic commerce right now.

This guide maps every significant agent trust platform in 2026 – their methodology, their real strengths, their genuine blind spots, and the specific signals that separate them. It is written for three audiences: DeFi protocol builders integrating agents and choosing a trust gating system, agent creators who want to understand how their agents get scored across platforms, and investors evaluating the agent trust infrastructure market as a sector.

Table of Contents

1. Why the Approach Matters More Than the Score
2. The Four Questions Agent Trust Platforms Answer
3. Platform 1 – ERC-8004 Native Reputation Registry
4. Platform 2 – RNWY: Review Quality and Sybil Detection
5. Platform 3 – SkyeProfile: Multi-Attestation Wallet Trust
6. Platform 4 – AXIS T-Score: Runtime Performance Scoring
7. Platform 5 – DJD Agent Score: Wallet Activity Scoring
8. Platform 6 – ChainAware: Behavioral Fraud Intelligence
9. The Five Signals Only One Platform Provides
10. Head-to-Head Comparison Table
11. Decision Matrix: Which Platform for Which Use Case?
12. The White Space: Five Capabilities Nobody Has Built Yet
13. The Investor Lens: What Makes Agent Trust Infrastructure a Durable Market
14. Frequently Asked Questions

Why the Approach Matters More Than the Score

Every agent trust platform in 2026 returns a number. The number is not the product – the threat model behind it is. Two platforms can both return a score of 72 for the same agent and disagree completely about what that score means, because they measured entirely different things to compute it.

RNWY’s score of 72 tells you: the agent’s peer reviews show limited sybil activity and the reviewer wallets are moderately established. ChainAware’s score of 72 tells you something different: the owner wallet has a moderate fraud probability, the feeder address is unknown, and no criminal record signals are present. SkyeProfile’s assessment tells you something different again: the wallet passes certain solvency and governance checks but shows limited behavioral depth across attestation providers.

Each score is internally consistent. However, each one answers a different question about the same agent. Consequently, the correct question for any DeFi protocol builder, agent creator, or investor is not “which platform gives the highest scores?” It is “which platform’s threat model matches the risk I am actually trying to prevent?”

For context on how this same problem appears at the wallet intelligence layer, see our complete guide to Web3 Wallet Auditing Providers in 2026 – the same principle applies there, where raw data providers, descriptive profilers, and predictive intelligence systems each answer fundamentally different questions about the same wallet address.

The Four Questions Agent Trust Platforms Answer

Before comparing platforms, mapping the question each approach addresses clarifies the landscape considerably. Every platform in the 2026 agent trust market falls into one of four categories based on what it actually measures.

Question 1: Have other agents endorsed this agent?

This is the peer review / reputation registry approach. The ERC-8004 native system operates here. Additionally, RNWY’s core methodology operates here – with the significant enhancement of reviewing the quality of the reviewers rather than simply counting the reviews. The fundamental limitation of this approach is that endorsement and trustworthiness are not the same thing. Any operator who controls multiple agents can engineer endorsements between them at near-zero cost.

Question 2: Has this agent performed tasks well?

This is the runtime performance approach. AXIS T-Score operates exclusively in this category, measuring 11 behavioral dimensions of agent task execution – completion rate, instruction adherence, error recovery, security posture, and similar metrics. The limitation here is that runtime performance and financial trustworthiness are orthogonal. An agent that executes tasks reliably can still be controlled by a fraud operator using it as a front for financial extraction.

Question 3: What does the agent’s wallet history look like?

This is the wallet activity approach. DJD Agent Score operates here, scoring seven wallet dimensions including transaction history, partner diversity, and account age. SkyeProfile’s solvency layer also operates here. The limitation is that wallet history describes the agent wallet itself – which is frequently a fresh address created specifically for the agent, with minimal history by design. A fresh agent wallet with no history is not the same as a fraudulent one, but wallet-only scoring treats them identically.

Question 4: Who controls this agent, and what have they done on-chain?

This is the behavioral fraud intelligence approach. ChainAware operates here – scoring the owner wallet that controls the agent, the feeder address that funded the owner, and cross-referencing both against a database of confirmed rug pulls and honeypot token creations. The threat model this addresses is the one that matters most for autonomous financial execution: a sophisticated fraud operator registering a new agent identity to continue activities previously conducted under different wallet identities.

Each of these four approaches is internally valid. Furthermore, they are not mutually exclusive – DeFi protocols can layer multiple approaches. However, understanding which question each one answers is essential before choosing which to gate on.

Platform 1 – ERC-8004 Native Reputation Registry

What it is

The ERC-8004 standard includes a built-in Reputation Registry as an optional component of the agent identity specification. The registry provides a standard interface for posting and fetching feedback signals. Critically, the standard explicitly does not define a scoring algorithm – aggregation and scoring are intentionally delegated to third parties. The protocol is infrastructure. Every other platform in this comparison is a third-party scoring layer on top of it.

Methodology

Any wallet can submit a feedback signal to the Reputation Registry for any registered agent. The signal includes a rating and optional metadata. The registry stores it on-chain. Reading platforms aggregate these signals according to their own methodology – which means the “ERC-8004 reputation score” is not a single consistent number but rather different outputs from different aggregation strategies across different platforms reading the same underlying data.

Strengths

The registry is permissionless, transparent, and composable. Any smart contract can read it. Furthermore, on-chain storage means the feedback history is permanent and verifiable. For building a decentralised reputation system in principle, the architecture is sound.

Blind spots

The fundamental blind spot is that the registry cannot distinguish manufactured reviews from genuine ones without an external intelligence layer on top. An operator controlling 50 agents can give each one a 5-star review from the other 49 at a cost of a few dollars in gas. Additionally, the native registry provides no information about who controls the agent, no feeder analysis, no fraud prediction, and no criminal record check. It answers only “what have other agents said about this agent?” – which is the weakest possible trust signal in a system where agents can be created and coordinated freely.

Who it is for

The native registry is appropriate as an additional data layer for platforms that have already implemented stronger trust signals. It should not serve as a primary trust gate for any DeFi protocol permitting autonomous financial execution.

Platform 2 – RNWY: Review Quality and Sybil Detection

What it is

RNWY (rnwy.com) is the most established third-party agent trust platform operating on ERC-8004 in 2026. RNWY positions itself as the trust layer for an economy where participants might not be human, with 185K+ agents scored and every score showing its math – the same door for humans and AI alike. The platform is notable for its transparency: all scoring methodology is published, including exact signal weights.

Methodology

RNWY’s trust score uses six input signals combined with additive modifier stacking, logarithmic value scaling, buffer zones, and evaluator softening to produce a score out of 95 across five tiers. The six signals weight toward reviewer quality rather than raw review count.

RNWY’s sybil detection applies four signals with explicit weights: common funder (6×), inhuman velocity (5×), sweep pattern (3×), and score clustering (1×). The weighted score produces severity levels: Low (0-2), Moderate (3-9), Elevated (10-19), and Heavy (20+). This makes RNWY’s sybil detection notably rigorous – it specifically targets the coordinated-review attack that would compromise naive review counting.

Since v1.1.0 (April 2026), RNWY also returns an owner wallet score, commerce summary (provider jobs, counterparty count, commerce tenure), and transaction-backed review percentage in the API response. However, these are additional intelligence fields – they appear in the response but do not affect the tier calculation or the primary trust score. This is the critical distinction from ChainAware: RNWY surfaces the owner wallet score as informational context; it does not integrate it into the scoring formula.

RNWY also indexes 1.7 million commerce jobs across Olas, Virtuals ACP, and SATI – making it the most comprehensive commerce activity tracker in the agent ecosystem. Trust scores live on Base mainnet, meaning any smart contract can read an agent’s score, tier, and sybil severity mid-transaction without an API call or oracle fee. This on-chain accessibility is a significant technical advantage for DeFi protocols that want to gate at the smart contract level rather than the application layer.

Strengths

RNWY’s strengths are transparency, on-chain accessibility, and commerce job history depth. The published methodology with exact signal weights means any relying party can independently verify a score calculation. The on-chain trust oracle on Base enables smart contract-level gating. The 1.7M commerce job index provides genuine economic activity context that no other platform matches. Additionally, the sybil detection is genuinely sophisticated – the common funder signal (weighted 6×) specifically targets the attack pattern of one operator funding multiple reviewer wallets from a single source.

Blind spots

RNWY’s primary blind spot is the boundary it draws at the review layer. The owner wallet score is surfaced but does not affect the tier. Feeder address analysis does not exist. Prior token creation history – rug pulls, honeypots – is not queried. Farm detection operates only at the reviewer level, not at the fleet level. Consequently, a fresh wallet that has never received a review (no positive signals, no negative signals) scores the same as an established operator in RNWY’s primary score calculation – both lack review history. Furthermore, a serial rug puller who has never participated in the ERC-8004 review ecosystem will not trigger any RNWY detection signal, because their fraud history exists in token creation, not in agent reviews.

Who it is for

RNWY is the strongest choice for platforms where agent reputation is displayed to users (marketplaces, directories, leaderboards) and where the primary threat model is manufactured peer endorsements. It is a compelling addition to any trust stack as a review quality layer. However, it is not sufficient as a standalone gate for DeFi protocols where the primary threat is a fraud operator using agents as the execution vehicle for financial crimes.

Platform 3 – SkyeProfile: Multi-Attestation Wallet Trust

What it is

SkyeProfile (skyemeta.com) is a multi-attestation wallet trust profile that orchestrates nine specialised attestation providers and returns one unified signed profile per wallet. The system uses a dual-score model with Signal Depth (behavioral observability) and Risk Intensity (sybil and fraud risk) as independent axes, covering 150K+ agents across ERC-8004, Olas, Virtuals, and SATI registries.

Methodology

SkyeProfile works on a general contractor model – eight dimensions, eight independent providers, eight verifiable signatures. One API call returns ten independently verifiable attestations, each with a JWKS URI so relying parties can verify any dimension offline without trusting SkyeMeta itself. The dimensions span solvency (wallet holdings across 33 chains), governance participation, behavioral trust, identity, security posture, compliance, performance, and settlement track record.

Notably, SkyeProfile uses RNWY as its behavioral trust provider – RNWY maintains the dual-score model across 150K+ agents spanning twelve EVM chains and Solana within the SkyeProfile attestation framework. This means SkyeProfile inherits RNWY’s methodology for the behavioral dimension, including both RNWY’s strengths (sybil detection, review quality analysis) and RNWY’s blind spots (no feeder analysis, no criminal record check, owner wallet informational only).

Strengths

SkyeProfile’s primary strength is breadth and verifiability. By aggregating nine specialised providers and returning independently verifiable signatures, it gives relying parties a comprehensive wallet profile that no single provider can match across all dimensions. The cryptographic verifiability (ES256 or EdDSA signatures with JWKS-published keys) is technically rigorous and appropriate for high-stakes autonomous execution contexts. The 33-chain solvency layer is the most comprehensive wallet holdings analysis in the market.

Blind spots

SkyeProfile scores the agent wallet – the address registered with the ERC-8004 identity. Since it delegates behavioral trust to RNWY, it inherits RNWY’s blind spots on feeder analysis and criminal record checking. Furthermore, because SkyeProfile is built as a wallet profiling system rather than an agent-specific fraud intelligence system, it does not perform fleet-level farm detection or trust delegation from owner to agent wallet. The platform also scores 150K agents across multiple registries – which is valuable breadth, but means its ERC-8004 specific coverage is thinner than RNWY’s 185K ERC-8004-specific indexed agents.

Who it is for

SkyeProfile is strongest for use cases requiring verifiable, multi-dimensional wallet attestations – particularly in contexts where cryptographic proof of each assessment matters, such as compliance audit trails or high-stakes DeFi credit decisions. For the broader DeFi credit scoring context, see our DeFi Credit Score Platform comparison. SkyeProfile is not a standalone agent trust gate – it is a comprehensive wallet profiling layer that serves agent trust as one of several use cases.

Platform 4 – AXIS T-Score: Runtime Performance Scoring

What it is

AXIS T-Score (axistrust.io) operates in an entirely different category from every other platform in this comparison. While all other platforms score the agent’s identity and on-chain history, AXIS scores the agent’s runtime behavior – how well it performs tasks, follows instructions, and operates within defined guardrails during actual execution.

Methodology

AXIS measures 11 behavioral dimensions: task completion rate, instruction adherence, data handling, transparency, error recovery, consistency, scope compliance, resource efficiency, communication clarity, security posture, and audit trail quality. All of these metrics are off-chain – they measure what the agent does during task execution, not what its controlling wallet has done on-chain. Scores run from 0 to 1,000 across five tiers (T1-T5), using the same 0-1,000 scale as ChainAware’s Agent Trust Score but measuring completely different inputs.

Strengths

AXIS addresses a genuinely different problem: does this agent do what it claims to do? An agent that claims to be a compliance screener but routinely fails to flag sanctioned addresses will score low on AXIS – regardless of how trustworthy its owner wallet is. That quality assurance dimension is valuable and not addressed by any on-chain behavioral platform. For enterprise contexts where agents are deployed for specific task categories, AXIS provides the most rigorous evaluation of task quality available.

Blind spots

AXIS scores runtime performance, not financial trustworthiness. An agent can score T5 on AXIS (top-tier task execution) and be controlled by a serial rug puller who has stolen millions. The two assessments are orthogonal – they address completely different threat models. For DeFi protocols where the primary concern is financial fraud rather than task quality, AXIS provides no relevant signal. Additionally, AXIS is entirely off-chain, which means it has no chain coverage, no wallet analysis, and no on-chain verifiability. Scores cannot be read by smart contracts and cannot be cryptographically verified against on-chain data.

Who it is for

AXIS is most valuable for enterprise deployments where agents perform specific workflow tasks – research, content generation, data analysis – and where task quality rather than financial fraud is the primary concern. Layering AXIS with an on-chain identity trust system (ChainAware for fraud intelligence, RNWY for review quality) produces the most complete agent evaluation stack: you verify both who controls the agent and how well the agent performs.

Platform 5 – DJD Agent Score: Wallet Activity Scoring

What it is

DJD Agent Score is the smallest and most narrowly focused platform in this comparison. It returns a 0-100 behavioral trust score for any wallet, combining seven dimensions – transaction history, partner diversity, volume patterns, account age, balance stability, activity consistency, and USDC usage – with sybil detection and gaming velocity checks. Scores feed directly into the ERC-8004 Reputation Registry as off-chain attestations, and the service is monetised via x402 micropayments in USDC on Base.

Methodology and coverage

DJD scores the agent wallet address across those seven dimensions. The scoring approach is transparent and the seven dimensions are reasonable wallet activity signals. However, coverage is Base-only – the platform does not index agents on Ethereum mainnet, BSC, or Avalanche. Furthermore, like SkyeProfile’s solvency layer and the ERC-8004 native registry, DJD scores the agent wallet rather than the owner wallet. This means it faces the same fresh wallet problem: a newly created agent wallet with no transaction history will score near zero on all seven dimensions regardless of the owner’s reputation.

Strengths and limitations

DJD’s x402 integration is technically interesting – it demonstrates a viable micropayment-based business model for agent trust scoring that does not require API keys or subscription agreements. The seven-dimension wallet scoring is simple, auditable, and directly verifiable. However, the Base-only coverage and agent-wallet focus rather than owner-wallet focus significantly limit DJD’s utility as a primary trust gate. It is best understood as an early-stage product demonstrating one viable approach rather than a production-ready trust infrastructure system.

Platform 6 – ChainAware: Behavioral Fraud Intelligence

What it is

ChainAware’s Agent Trust Score approaches agent trust from the opposite direction of every other platform. Rather than starting from the agent and asking what signals the agent produces (reviews, task performance, wallet history), ChainAware starts from the human behind the agent and asks what that human has done on-chain across their entire history – including activities completely unrelated to the current agent registration.

This inversion is the foundation of every signal that differentiates ChainAware from the rest of the market. For a full technical explanation of the scoring formula, see the Agent Trust Score methodology page.

Core formula

The Agent Trust Score builds on the same Wallet Reputation Score formula used across ChainAware’s products:

ReputationScore = (1000/110) × (experience + 1) × (risk_capability + 1) × (1 − fraud_probability)
Maximum: 1,000

This formula runs separately on the owner wallet and the agent wallet. Furthermore, it runs on the feeder address when traceable. The results are then combined using trust delegation logic, farm detection modifiers, and criminal record hard caps to produce the final Agent Trust Score. The 0-1000 scale is consistent with the Wallet Reputation Score – meaning a protocol that already uses ChainAware’s wallet intelligence can compare agent trust and wallet trust on the same axis without recalibration.

Coverage and infrastructure

ChainAware indexes 240,000+ ERC-8004 agents across Ethereum mainnet, BSC, Base, and Avalanche – the widest chain coverage in the market for a predictive fraud intelligence approach. The underlying wallet persona database covers 20M+ addresses across 8 blockchains, trained on behavioral data accumulated over multiple years. The fraud prediction model achieves 98% accuracy on held-out test data, as documented in our AI-Powered Blockchain Analysis guide. Additionally, scores are available via the Prediction MCP server, meaning any Claude-based DeFi agent can query agent trust scores as a native tool call without custom API integration.

The Five Signals Only One Platform Provides

Five signals in the Agent Trust Score are not replicated by any other platform currently operating in the ERC-8004 agent trust market. Each one addresses a specific threat model that the other approaches structurally cannot reach.

Signal 1: Feeder address analysis

The feeder address is the wallet that funded the agent’s owner wallet. Tracing and scoring it is the single most distinctive capability in the ChainAware Agent Trust Score. No other platform – not RNWY, not SkyeProfile, not DJD – performs feeder analysis.

Why it matters: an experienced fraud operator rotates owner wallets between campaigns. Wallet A runs a rug pull, gets flagged, and is abandoned. Wallet B is freshly created and funded from Wallet A. Wallet B then registers 40 agents on ERC-8004. Every platform that scores only the agent or the agent’s direct owner wallet will see a clean Wallet B with no fraud history. ChainAware traces the funding path and scores Wallet A – the feeder – which carries the fraud record. Wallet B’s agents receive hard-capped scores regardless of how clean Wallet B’s own history appears.

ChainAware covers feeder analysis for approximately 38% of indexed agents – the ones with a traceable single-hop funding source. For agents where the feeder is a verified CEX withdrawal address (Binance, Coinbase, Kraken, OKX), the platform flags this as FEEDER_CEX_VERIFIED – a positive trust signal that implies the owner wallet was funded via a KYC’d exchange withdrawal. For agents where the feeder is unknown or obfuscated, the platform applies a penalty reflecting the information asymmetry.

Signal 2: Criminal record – rug pull history

ChainAware maintains a database built from one year of on-chain liquidity pair history. That database records which wallet addresses created pools that subsequently exhibited rug pull patterns – rapid liquidity removal after price appreciation, following the operational signature documented in our Rug Pull vs Pump and Dump guide.

Before computing the Agent Trust Score, ChainAware cross-references both the owner wallet and the feeder address against this database. A single confirmed rug pull in the owner’s history generates a hard cap on the Agent Trust Score – a ceiling no other signal can override. This is the signal that connects yesterday’s token fraud to today’s agent deployment. An operator who rugged three pools on PancakeSwap in Q4 2025 and registered 40 agents in Q1 2026 is caught by this check. No other agent trust platform makes that connection, because no other platform maintains a paired rug pull database and cross-references it against agent registry data.

Signal 3: Criminal record – honeypot token history

Separately from rug pull detection, ChainAware maintains token audit data identifying honeypot contracts – tokens with embedded code that prevents buyers from selling. The creator wallet for each identified honeypot token is recorded. Cross-referencing agent owner wallets against this database produces a second criminal record dimension: has the agent’s controller previously created trap tokens that extracted funds from retail investors?

Honeypot creation and rug pull creation are related but distinct fraud patterns. Some operators specialise in one or the other; some use both. Having both databases cross-referenced produces a more complete criminal record than either alone. Together with rug pull history, this gives ChainAware the only criminal record check available in the agent trust market. For more on how token auditing produces these signals, see our Token Audit methodology.

Signal 4: Trust delegation

Trust delegation is ChainAware’s mechanism for handling the fresh agent wallet problem without penalising legitimate new agents. Agent payment wallets are frequently created specifically for an agent deployment – they are fresh addresses with no transaction history. A scoring approach that treats wallet age as a primary negative signal would incorrectly assign low trust to every newly deployed agent from a legitimate operator.

ChainAware’s trust delegation sets a floor for the agent wallet’s effective score based on the owner wallet’s Reputation Score. A strong owner (Sovereign tier, 800+) partially transfers credibility to the fresh agent wallet, resulting in a significantly higher Agent Trust Score than the agent wallet alone would produce. A fraud-flagged owner, by contrast, cannot delegate any meaningful trust – the delegation factor collapses to near zero. This means fresh wallets from reputable operators score correctly high, and fresh wallets from fraud operators score correctly low – which is the right outcome for both cases.

Signal 5: Fleet-level farm detection

Every other platform in this comparison scores agents individually. ChainAware maintains an owner profile database – tracking how many agents each owner controls across all indexed chains and whether those agents were registered in the same block (indicating automated bulk registration). This fleet-level view enables detection of agent farms that individual agent scoring cannot surface.

An operator running a farm of 50 agents will have each individual agent score independently on RNWY, SkyeProfile, or DJD. Nothing in those individual scores reveals the coordinated nature of the fleet. ChainAware sees the fleet. Owners controlling anomalously large numbers of agents receive a suppression modifier that applies to every agent in their fleet – including agents that individually might score cleanly. This is the signal that catches the specific agentic commerce attack pattern identified in our Blockchain Data Providers guide: one operator manufacturing ecosystem depth through controlled agent populations.

Head-to-Head Comparison Table

The following table compares all six approaches across every dimension relevant to DeFi protocol builders and investors evaluating the space. Each row describes a specific capability, not a general category, to make the comparison as concrete as possible.

Decision Matrix: Which Platform for Which Use Case?

No single platform is the correct choice for every context. The right stack depends on what you are trying to prevent, what signals matter for your specific use case, and what integration constraints you are working within. The following matrix maps use cases to recommended platform combinations.

DeFi protocol gating autonomous financial execution

Primary: ChainAware Agent Trust Score – owner fraud probability, feeder analysis, criminal record check, and farm detection are all directly relevant to the threat model. Set tier thresholds based on transaction risk: Trusted (600+) for high-value operations, Provisional (400+) for lower-risk flows with monitoring.

Secondary: RNWY for reputation display – show the RNWY score in your protocol’s agent directory alongside the ChainAware score. They answer different questions and the combination is more informative than either alone.

Optional: SkyeProfile attestations if your compliance framework requires cryptographically verifiable attestations as audit evidence. For the compliance context, see our DeFi Compliance and AML guide.

Agent marketplace or directory

Primary: RNWY – the on-chain trust oracle on Base enables smart contract-level minimum score requirements for job listing. The commerce job history (1.7M jobs) is directly relevant to marketplace quality filtering. The transparent published methodology means marketplace users can understand exactly why an agent scores as it does.

Secondary: ChainAware Agent Trust Score – surface it as a fraud intelligence layer alongside RNWY’s reputation score. The two scores are complementary: RNWY tells users whether the agent’s reviews are genuine; ChainAware tells users whether the human behind the agent has a history of financial fraud.

Enterprise workflow agent deployment

Primary: AXIS T-Score – for enterprise agents performing specific workflow tasks (research, compliance screening, content generation), task quality assurance is the primary concern. AXIS is the only platform that evaluates whether an agent does what it claims to do.

Secondary: ChainAware if the agent has financial execution permissions. Task quality and financial trustworthiness are both relevant for agents with write permissions to financial systems.

Agent creator wanting to understand their score

Agent creators interact with multiple trust systems simultaneously. Your agents are scored by every platform a buyer chooses to query. Understanding all five is therefore more important for creators than for buyers. Specifically:

• RNWY score: ensure your agent has genuine reviews from established reviewer wallets. Avoid requesting reviews from wallets that bulk-review across many agents – they will be detected as sybil reviewers and suppress your score
• ChainAware score: your owner wallet’s history is the primary input. A wallet with 12+ months of diverse DeFi activity scores significantly higher than a fresh wallet. If your feeder is a CEX withdrawal, this is a positive signal that surfaces automatically
• SkyeProfile: ensure your owner wallet holds governance tokens and participates in established protocols – the solvency and governance dimensions reward breadth of DeFi participation
• AXIS: if you want T-Score evaluation, ensure your agent returns reliable, consistent outputs and maintains audit trail quality across repeated task executions

The White Space: Five Capabilities Nobody Has Built Yet

The current agent trust infrastructure market is six months old. Consequently, significant white space remains – capabilities that no platform currently provides but that the market will almost certainly require as agentic commerce scales. The following five gaps represent the next investment and product opportunities in this category.

Gap 1: Agent-to-agent trust propagation

No platform currently answers this question: if Agent A scores Sovereign and has completed 10,000 successful interactions with Agent B, does that interaction history update Agent B’s trust score? In human systems, ongoing positive relationships build trust over time. In agent systems, every score is computed from static inputs without accounting for the accumulated interaction history between specific agent pairs. Building trust propagation that flows through agent interaction graphs – raising Agent B’s score based on verified positive interactions with high-scoring agents – would fundamentally change how trust compounds in the agentic economy.

Gap 2: Cross-registry agent identity resolution

An operator may deploy agents across ERC-8004, Olas, Virtuals, and SATI simultaneously. Currently, each registry treats these as separate identities. No platform provides unified entity resolution – grouping agents across registries that share the same owner wallet into a single entity profile. This matters because fleet-level behavior visible at the entity level (100 agents across 4 registries controlled by one owner) is invisible at the per-registry level (25 agents on each).

Gap 3: MCP server trust scoring

Every agent trust platform scores the agent itself. None score the MCP servers the agent calls. An agent connecting to a malicious or compromised MCP server is a trusted agent performing untrusted actions. As the MCP ecosystem grows – Smithery already indexes thousands of MCP servers – the trust question extends naturally from “who is the agent?” to “what tools is the agent using?”

Gap 4: Trust score-based insurance underwriting

No DeFi insurance protocol currently uses agent trust scores as an underwriting input. A protocol granting autonomous execution access to a Sovereign-tier agent (800+) takes on less risk than one granting the same access to a Provisional-tier agent (400-599). Insurance premiums, coverage limits, and deductibles could all be parameterised on agent trust scores – creating a financial market that prices the residual risk after trust gating rather than treating all agent access as equally risky.

Gap 5: Dynamic trust scores updating in real time

Current trust scores are computed at query time from static inputs and cached. None update continuously as new on-chain events occur. An agent whose owner wallet executes a suspicious transaction pattern at 14:00 UTC will not have its trust score updated until the next scoring cycle. Real-time trust score streaming – where scores update within seconds of relevant on-chain events – would enable dynamic access control that responds to emerging fraud signals rather than lagging behind them.

The Investor Lens: What Makes Agent Trust Infrastructure a Durable Market

For investors evaluating the agent trust infrastructure category, several structural dynamics shape the market’s long-term economics.

The TAM compounds with agent adoption

Agent trust infrastructure is a derived demand market – its TAM scales directly with agentic commerce adoption. McKinsey’s $3-5 trillion agentic commerce estimate implies that every dollar of economic activity flowing through autonomous agents creates a corresponding demand for trust verification of those agents. As Morgan Stanley projects , nearly half of online shoppers will use AI shopping agents by 2030. Each one of those agents represents a trust decision for every protocol or merchant it interacts with.

Consequently, market growth in agent trust infrastructure is structurally tied to the overall growth of agentic AI – a market with multiple large tailwinds including regulatory pressure (Know Your Agent protocols emerging from the EU AI Act framework), enterprise adoption (agents handling financial workflows requiring documented risk controls), and protocol incentives (DeFi protocols facing liability exposure from agent-initiated fraud).

Data network effects favour early movers with behavioral databases

Agent trust platforms that rely on behavioral databases – rather than purely algorithmic or review-based scoring – accumulate a compounding data advantage. A platform with one year of on-chain pair history knows which wallets created rug pools. A platform with two years knows which wallets have repeat patterns across multiple fraud campaigns. That historical depth cannot be compressed – a competitor starting today cannot buy the historical database that an early mover has built through continuous operation.

This dynamic differentiates behavioral fraud intelligence platforms from review-quality platforms. RNWY’s review quality algorithm could theoretically be replicated by a well-resourced team in months. The underlying behavioral database and fraud prediction model trained on years of on-chain data cannot. For context on how machine learning model development timelines apply to this space, see our AI-Powered Blockchain Analysis guide.

Complementary rather than winner-takes-all

The four distinct approaches in the market address different threat models that do not fully substitute for each other. RNWY’s review quality signal and ChainAware’s behavioral fraud intelligence are complementary – a protocol using both is better protected than a protocol using either alone. This means the agent trust market is likely to support multiple sustainable businesses serving different parts of the trust stack, rather than converging to a single dominant platform.

The parallel is the credit rating market – Moody’s, S&P, and Fitch coexist because rating agencies with complementary methodologies provide more value to the market than a single monopoly. Agent trust infrastructure may evolve similarly, with different platforms serving different trust dimensions in a layered stack. For investors, this implies that both the review quality layer (RNWY) and the behavioral fraud intelligence layer (ChainAware) have independent market positions rather than competing for the same slot in every protocol’s integration.

Regulatory tailwinds

The EU AI Act , which takes full effect in August 2026, creates documentation and risk assessment requirements for high-risk AI systems. Autonomous agents with financial execution permissions are a clear candidate for high-risk classification under this framework. Protocols operating in EU-regulated markets will need demonstrable risk controls for agent interactions – a requirement that agent trust scoring infrastructure directly satisfies. Additionally, Know Your Agent (KYA) protocols are emerging as the agent-layer equivalent of KYC, creating a compliance-driven pull for trust verification infrastructure beyond pure product adoption.

Frequently Asked Questions

Can I use multiple agent trust platforms simultaneously?

Yes – and for high-value use cases, this is the recommended approach. RNWY and ChainAware answer different questions about the same agent. Using RNWY for review quality and ChainAware for owner fraud intelligence produces a more complete picture than either alone. The integration is straightforward: make two API calls per agent and combine the results in your access control logic. DeFi protocol builders can set independent thresholds for each score – for example, requiring RNWY tier 3+ (genuine review history) AND ChainAware Trusted tier (600+) for full autonomous execution access.

Which platform is best for displaying trust to end users?

RNWY is the strongest choice for trust display because its methodology is fully published and each score shows its math. End users can understand exactly why an agent scored as it did – which reviewer wallets were flagged, which sybil patterns were detected, what the address age contributed. Transparency builds user confidence. ChainAware’s score is complementary but its weights are private (to prevent gaming), making RNWY more appropriate for user-facing display where explainability matters.

How do the different score scales compare?

The score scales are not directly comparable across platforms. RNWY scores out of 95 (not 100 – their maximum is 95 due to scoring mechanics). ChainAware and AXIS both use 0-1,000. DJD uses 0-100. SkyeProfile uses two independent axes rather than a single number. Converting between scales requires understanding what each platform actually measures, which is why the comparison table above focuses on capabilities rather than score values. An agent scoring 72/95 on RNWY and 650/1,000 on ChainAware is not inconsistent – those numbers describe entirely different assessments.

Does RNWY’s owner wallet score compete with ChainAware?

Not meaningfully. RNWY’s v1.1.0 update added owner wallet score as an informational field in the API response – but explicitly does not affect tier calculation or the primary trust score. The field surfaces the owner wallet’s RNWY-defined score as context for relying parties who want to incorporate it into their own decision logic. ChainAware makes the owner wallet score the primary input to the Agent Trust Score formula, combines it with feeder analysis and criminal record data, and applies trust delegation. The two approaches share the observation that owner wallet matters – but diverge completely on how to score it and how much weight it should carry.

What is ERC-8183 and how does it relate to agent trust?

ERC-8183 is a commerce protocol that gives AI agents trustless commerce capabilities – handling escrow, state transitions, and evaluator attestation for agent-to-agent job markets. The spec is intentionally minimal – it handles the commerce mechanics but explicitly does not handle trust scoring, discovery, or fraud detection. RNWY has built a marketplace and trust scoring layer on top of ERC-8183. ChainAware’s Agent Trust Score is compatible with ERC-8183 job markets as a pre-interaction trust gate – protocol teams can require a minimum Agent Trust Score before an agent can claim a job or receive escrowed funds.

How often do trust scores update?

Update frequencies vary by platform. ChainAware’s fraud prediction model retrains daily – meaning the fraud probability feeding into owner wallet scores updates continuously as new on-chain patterns emerge. Scores for specific agents update when new relevant events are detected (new agent registrations, owner wallet activity, feeder transactions). RNWY scores update as new reviews are submitted to the ERC-8004 Reputation Registry and as sybil analysis runs on reviewer wallets. AXIS T-Score updates based on runtime task execution data. None of the current platforms offer real-time streaming score updates – that remains a white space capability described above.

Is the ChainAware Agent Trust Score relevant for non-ERC-8004 agents?

Partially. The owner wallet and feeder address scoring works for any wallet address, regardless of whether it is associated with an ERC-8004 registration. A protocol that receives agent-initiated transactions from wallets not registered on any standard identity registry can still query ChainAware’s Fraud Detection API for the controlling wallet’s behavioral intelligence. The ERC-8004-specific signals (farm detection, trust delegation from registry data) require an ERC-8004 registration to function. However, the owner fraud probability, feeder analysis, and criminal record check work on any wallet regardless of registry status. For protocols on chains not yet covered by ERC-8004 registries, this means ChainAware provides partial Agent Trust Score functionality even before full ERC-8004 adoption on those chains.

Where can I read ChainAware’s full scoring methodology?

The complete methodology – including the five scoring layers, all flag definitions, score tier descriptions, and the trust delegation formula – is documented at chainaware.ai/learn/agent-trust-score. The signal categories are published. The exact weights and model coefficients remain private to prevent gaming. The equivalent documentation for the underlying Wallet Reputation Score (which feeds into the Agent Trust Score formula) is at chainaware.ai/learn/for-individuals/wallet-auditor.

Further Reading

• Agent Trust Score – Complete Methodology – the five scoring layers, all flags, tier definitions, and trust delegation formula
• The First Step in Agentic Commerce Isn’t Integration. It’s Trust. – the companion article covering the trust gap in DeFi protocol agent integrations
• Web3 Wallet Auditing Providers in 2026 – the same three-layer framework applied to the wallet intelligence market
• Web3 Analytics Tools for Dapps: Complete Comparison – where agent trust scoring fits in the broader DeFi analytics stack
• AI-Powered Blockchain Analysis for Crypto Security – the machine learning methodology behind ChainAware’s 98% fraud detection accuracy
• Rug Pull vs Pump and Dump – the fraud patterns that generate ChainAware’s criminal record database
• DeFi Compliance: KYT and AML Guide 2026 – regulatory context for DeFi agent integration compliance
• DeFi Credit Score Platforms Compared – how agent trust scoring combines with borrower creditworthiness assessment
• Prediction MCP Setup Guide – add ChainAware behavioral intelligence including Agent Trust Score to any Claude agent
• 32 Ready-Made Agents – pre-built Claude agents including agent verification, fraud detection, and compliance screening

ChainAware.ai is the Web3 Agentic Growth Infrastructure – behavioral intelligence for DeFi protocols, AI agents, and individual crypto users. 20M+ wallet personas, 98% fraud detection accuracy, <100ms API latency across 8 blockchains. Try free at chainaware.ai.

The post The Agent Trust Infrastructure Race: Who Is Building the Trust Layer for Agentic Commerce? first appeared on ChainAware.ai.