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Operational Readiness: Registered != Live != Getting Paid

Independent research and core Ethereum infra are now converging on the same question our whole product surface has been measuring. Our all-Ethereum index of 25,618 ERC-8004 agents across Base and Ethereum mainnet, of which 3,323 actually answer at their advertised endpoints and 44 carry a ReputationRegistry row that ties back to a real, paid on-chain job, is the empirical dataset that answers it.

Published 31 July 2026 · Headline counts on this page are pulled live from our public /v1/public/stats endpoint at build time and refreshed on every visit.

Two things landed in ERC-8004 world at almost the same moment. The first is an arXiv preprint — 2606.12128, From Agent Identity to Agent Economy: Measuring the Operational Readiness of ERC-8004 AI Agents by Rischan Mafrur and Priagung Khusumanegara — that names the exact quantity we've been treating as the load-bearing number since Report 01: not registration count, not attestation count, but operational readiness. The second is The Graph Foundation's write-up of x402 and ERC-8004 as paired primitives, framing indexed cross-chain reputation lookup as the practical shape of an inter-chain agent economy.

Both pieces circle the same problem from opposite directions. Independent researchers are asking "how many of these registered agents are actually doing anything?" and core infra is asking "what do you need to build for the ones that are?" The register-vs-live gap stops being a niche complaint and becomes the shared frame for the next wave of ERC-8004 work.

We publish the on-chain dataset that answers it. This post takes both of those external artifacts at face value, cites what they actually say, and shows where our commerce-backed cohort and per-chain live-rate audit fit alongside them as complementary empirical evidence.

What the arXiv paper actually measures

From Agent Identity to Agent Economy studies the first ten thousand ERC-8004 agent IDs on Ethereum mainnet over a bounded block-range window (roughly 29 January to 9 April 2026). Rather than proposing a single "operational" flag, the authors build a layered evidence framework and score every agent zero-to-five on observable signals: metadata availability, service endpoint declarations, ReputationRegistry feedback records, cross-chain registration, and transfer activity. The methodology combines feature engineering with network analysis over the owner-agent, feedback-client, and wallet-transfer graphs.

The paper's headline finding is that early adoption is "registration-heavy but operationally shallow". The published funnel from the abstract and body:

  • Only 67 agents (0.67%) declare service records.
  • Only 628 agents (6.28%) have any ReputationRegistry feedback at all.
  • Only 19 agents (0.19%) satisfy every layer of the framework — metadata plus services plus feedback plus cross-chain evidence.
  • The top ten owner wallets hold 51.4% of the full ten-thousand-agent slice. The single largest feedback client accounts for 65.8% of all feedback records.

The paper is empirical rather than prescriptive. The authors do not pick winners; they describe a distribution. But the distribution they describe is the same one every serious integrator has been running into for months: raw registration numbers are cheap and misleading, and the honest signal lives in a much smaller cohort behind several layers of evidence.

Two things about this we have been publishing continuously from a different angle. First, "service records" in the paper's sense — an agent that declares a service — is a subset of the stricter test we run in production, where an agent must not only declare an endpoint but also return 2xx on it and expose a machine-readable surface (MCP, OpenAPI, or /.well-known/agent-card.json). Second, the paper's "reputation feedback" count is unfiltered by Sybil concentration — the 65.8% concentration in a single feedback client is exactly the pathology our commerce-backed predicate was designed to filter around. The paper's raw feedback figure is the pre-filter number; the commerce-backed figure we publish is the post-filter number for the same registry.

What The Graph Foundation's post frames

Understanding x402 and ERC-8004 (The Graph Foundation, 5 February 2026) takes the paired standards at their intended use case. ERC-8004 provides verifiable on-chain identity for agents; x402 provides the HTTP-native micropayment settlement layer that lets those agents transact autonomously. The post's operational angle is that indexed cross-chain reputation lookup is what makes the pair usable in practice — "an AI agent operating on Base can instantly verify the reputation of an agent on Arbitrum by simply querying a Subgraph."

The post covers eight chains, with Base and Arbitrum called out as the working examples. The Graph's role in the framing is the data infrastructure that makes cross-chain reputation queries fast enough to matter, and GraphTally is the micropayment layer that makes the indexer economics work at agent scale.

Two takeaways for anyone building on top. One: the ERC-8004 + x402 stack is being positioned as inter-chain by default, not single-chain-first. Two: fast, reliable indexed access to the underlying registry state is the piece the ecosystem is treating as the missing primitive.

Both are directly downstream of the register-vs-live-vs-paid distribution. A cross-chain reputation lookup that returns "yes, this agent has reputation" is worth much less if the agent behind the row cannot answer a request or has never been paid. The indexer is necessary; the trust filter on top of it is what makes the answer useful.

Where our data fits

Our per-chain, live-probed dataset is the direct empirical complement to both pieces. The paper counts registrations, declarations, and feedback rows across one chain and one 10k slice; our index runs continuously across two chains and pairs each registered row with an active liveness probe and a commerce-backed predicate:

Measure (live, all-Ethereum index) Value
ERC-8004 agents indexed (Base + Ethereum) 25,618
Live agents (endpoint 2xx AND capability declared) 3,323
Live rate — Base 7%
Live rate — Ethereum mainnet 58%
ReputationRegistry feedback events indexed 277,710
Agents that have received any feedback at all 29,128
Commerce-backed agents (got-paid cohort) 44
Job-outcome feedback rows (T1 / T3 evidence) 979

Live-source: /v1/public/stats. Liveness itself has been continuously tracked since 2026-07-17.

The two datasets sit next to each other cleanly. The paper's "service records" layer is the declared-endpoint predicate; our live number is that same layer verified by a real request. The paper's raw feedback count is the ReputationRegistry state pre-filter; our commerce-backed number is that same state post-filter, where the filter requires the counterparty to have actually paid the agent through an ERC-8183 AgentCommerce job outcome or the Virtuals ACP hosted equivalent. The concentration pathology the paper documents (one client responsible for 65.8% of all feedback) is why the raw count is not the number to shop by — a Sybil farm can produce endorsements at zero cost, but cannot produce a commerce-backed row without first paying the agent, at which point it is a paying customer.

Complementary, not competitive

The arXiv authors are doing academic measurement. The Graph Foundation is doing infrastructure positioning. We publish a live, commercially operated dataset that either can build on and cite. The three angles are not the same product; they answer three different questions:

  • The paper answers "what does the observable evidence distribution look like on Ethereum mainnet for the first 10k agents?"
  • The Graph post answers "what does inter-chain reputation lookup need from an indexer to be practical?"
  • Our index answers "for any specific agent right now, on Base or Ethereum mainnet — is it live, has it been paid, and who has it worked with?"

The through-line is operational readiness. Registered is the cheap bar; live is the medium bar; commerce-backed is the honest bar. Any downstream product that has to pick agents to hire, route payments to, or trust with jobs has to get past all three.

Three commerce-backed agents you can inspect right now

Every one of these free per-agent pages carries the readiness bucket, live-endpoint composition, capability tags, and commerce-backed status the paper's framework would score on. Follow the links and see the same signals a paid /v1/intel/agent response is built from:

  • Lunara (Base) — one of the deepest job-outcome rows in the current commerce-backed cohort.
  • Ethy AI (Base) — hosted ACP integration with an actively growing job trail.
  • Capminal (Base) — another top-of-cohort commerce-backed profile.

The Ethereum-mainnet side of the same index is browsable from the Ethereum chain brief, and every Ethereum-mainnet ERC-8004 agent has a matching free teaser under /app/agent/ethereum/{id}.

The trust filter, not the registry dump

The line the series has been walking since Report 01 is the same one the paper and The Graph post are now walking from their own angles. Anyone can count Transfer(from=0x0). Very few can publish, for any specific agent, a probe-verified liveness answer and a commerce-backed reputation status. The paper's 0.19% "full-evidence" figure and our commerce-backed cohort are the same story told with two different filter stacks — and both converge on the same conclusion: the useful signal in ERC-8004 is a small, filtered, observable slice, and surfacing it is the piece the ecosystem now needs to build around. The same trust-filter cut applied to the wider x402 paid-endpoint catalog lives on our live x402 Bazaar market map, which buckets Coinbase's public CDP discovery API by unique-payer thresholds over the last 30 days — the same "is anyone actually paying this endpoint" question, one layer up.

Walk the got-paid cohort — free

Every commerce-backed agent has a free per-agent page with its job count, readiness bucket, and current live-endpoint composition. The /commerce-backed-agents hub ranks the cohort by on-chain job-outcome count.

See the cohort → Lunara (Base) → Ethy AI (Base) → Capminal (Base) →

Cite the operational-readiness dataset

Researchers and integrators are welcome to cite our data. The paid /v1/intel/agent response carries the Sybil-adjusted reputation score, commerce-backed status and job-outcome count, hook-type classification, by-service revenue breakdown, and 7d / 30d velocity delta over the commerce subset — for any agent on Base or Ethereum mainnet. Payable per call in USDC or ETH via x402, or unmetered on a Pro subscription. The free /v1/public/stats endpoint powers every headline count on this page.

See the API → Browse leaderboards Live ecosystem stats Related: Report 03 Related: Report 04