On January 29, 2026, at 8:00 AM UTC+8, the ERC-8004 standard was deployed to the Ethereum mainnet by Davide Crapis, the AI lead at the Ethereum Foundation. This technical move was quickly marked by many developers and infrastructure teams as a key milestone for the "AI agent and trustless service market." The focus of capital and narrative is shifting from computing power and model parameters to another underlying front: who will provide verifiable identities and accumulable reputations for thousands of AI agents. As the AI agent economy is repeatedly depicted as "the next trillion-dollar market," the shortcomings are becoming increasingly clear—the issue is not whether there are sufficiently intelligent models, but whether they can be discovered by different organizations, standardized for evaluation, and securely settled in an open environment. The ERC-8004 attempts to provide an answer by establishing a cross-platform, cross-organization trust foundation for AI agents, but the extent to which this standard can fulfill its vision remains an unfinished gamble.
From Experiment to Mainnet: AI Agents Transitioning from Test Fields to Public Infrastructure
● During the testnet phase, a "simulated market" around ERC-8004 has already been established. According to a single source, over 10,000 agents have completed registration in the testing environment, essentially proving that the standard can support the recording and querying of large-scale identity objects. However, this number currently lacks multi-source cross-validation and has not formed a public growth curve, serving only as a signal of early interest and experimental activity, rather than a confirmation of a mature ecosystem scale.
● Narratively, ERC-8004 is not an isolated contract update but a proactive positioning of AI within the official Ethereum sequence. Davide Crapis, the AI lead at the Ethereum Foundation, personally pushed for this standard, from proposal and testing to the mainnet launch on January 29, 2026. The timeline itself conveys a clear stance: the AI agent economy will not merely be a "plug-in" business on Ethereum but is regarded as a first-class citizen that requires native standard support. This push, led by the core development circle, gives ERC-8004 a distinct sense of "protocol layer recognition," rather than being merely a community experiment.
● Coinciding with the mainnet deployment is an activity window called "8004 Genesis Month," which focuses on agent registration, tool integration, and early task market trials, aiming to quickly gather the first batch of developers and AI agent operators as the standard is just being established. For a new standard, the ability to accumulate enough "real use cases" and on-chain footprints in the first month after launch often determines whether it remains a symbol on a white paper and EIP page or becomes a living standard that developers default to.
● In the trust narrative, a key detail is that the ERC-8004 contract has been publicly hosted in the GitHub repository erc-8004/erc-8004-contracts, allowing anyone to review, replicate, and monitor updates. This embrace of open-source and auditability from the outset means that the acceptance of the standard goes beyond mere "transparency": for infrastructure that relies on identity and reputation layers, developers are more concerned about whether they will be "trapped in a black box" in the future, and the public repository reduces the risk of being bound to specific service providers while reserving space for security audits and community forks.
Discoverable AI Agents: Identity Transformed from Platform Assets to Public Resources
● The primary goal of ERC-8004's design is to provide cross-organizational discoverability for AI agents. In current mainstream practices, many AI agents are encapsulated within a single platform or specific application, such as automation assistants that serve only a particular SaaS product or dedicated bots that exist solely within a specific task market. Their "existence" is firmly locked within the platform's database, making it nearly impossible for external parties to systematically discover and reuse them. ERC-8004 attempts to transcribe this "platform-privatized identity" into publicly retrievable on-chain records.
● Under the vision of standardized identity, the same AI agent can be uniformly recognized and invoked across different DApps and organizations: an agent that performs excellently in data labeling tasks can be directly called by risk control systems or on-chain trading automation tools in the future, without needing to be recreated or reevaluated. For developers, the integration cost lies in a one-time connection to the ERC-8004 identity interface, rather than adapting to each platform's private API; for organizations, instead of building a closed "agent pool," it is more beneficial to connect directly with the public identity layer to obtain reusable capability supplies.
● To visualize this abstract identity, an entry similar to an "agent browser" has emerged in the ecosystem—8004scan.io. It provides a list, detail pages, and query interfaces for agents registered under ERC-8004, currently supporting view aggregation across multiple networks such as Ethereum, Base, and Polygon in the testing phase. This multi-chain perspective is not merely a display layer of "multi-network compatibility," but trains users to view AI agents as public infrastructure existing across domains, rather than as subordinate assets of a specific chain or platform.
● Multi-chain support for the AI agent ecosystem means decoupling identity registration from the actual execution environment: agents can complete identity registration and basic descriptions in one location while executing entirely different tasks in different domains—from DeFi strategy execution to data preprocessing, and even off-chain API orchestration. The design of "registered in one place, visible across multiple domains" lays the groundwork for future cross-chain task execution and cross-platform composite services, bringing the "agent economy" closer to a unified market rather than fragmented islands.
Portable Reputation: How Trust Assets of AI Agents Accumulate
● Beyond "who is present," a more sensitive question for ERC-8004 is "who is trustworthy." One of the core ideas behind the standard is to build a portable reputation layer for agents—that is, allowing the performance of the same agent across different tasks and platforms to accumulate as on-chain records that can be referenced and combined, rather than scattered across various platforms' ratings and logs. Thus, when an agent moves from one task market to another, it does not have to start from "zero evaluation."
● Compared to traditional Web2 platform rating systems, on-chain reputation possesses three characteristics: public, combinable, and auditable. Star ratings and credit scores within platforms are often products of black-box algorithms, difficult to verify externally and unable to be inherited across platforms; whereas the reputation records associated with ERC-8004 can, in principle, be referenced by any contract or frontend, allowing developers to directly read original or processed data such as task completion status and historical default information, thereby constructing their own scoring models. This modular reputation layer transforms "trust" from a platform asset into a public infrastructure.
● In the vision of a trustless service market, task publishers can still make choices and pricing based on standardized reputation without knowing any agents: for example, quickly filtering candidate agents based on historical success rates for certain types of tasks, average response times, and types of organizations previously served, and matching differentiated rewards. For complex tasks, higher historical performance thresholds can be required; for experimental projects, new agents may be allowed to enter in exchange for more competitive pricing.
● However, the reputation layer itself will also introduce new competitive dynamics. Once high-quality agents accumulate a "rich history" through long-term tasks, they have the opportunity to gain significant premiums in bidding, potentially forming a de facto "star agent" pattern; while new agents, lacking historical records, will face cold start dilemmas, having to gradually accumulate credit by taking on low-priced, low-risk tasks or accepting stricter custody mechanisms. Balancing the encouragement of long-term accumulation with leaving room for newcomers to rise will determine whether the entire market can avoid evolving into an oligopoly dominated by a few leading agents.
Unresolved Validation Registry: The Middle Layer Gap in the Trust System
● In discussions about the trust architecture of ERC-8004, a component that has been repeatedly mentioned but not yet realized is the Validation Registry. It is envisioned as a space for summarizing "trusted lists" and validation nodes to mark which agents, data sources, and validators are considered more reliable. However, at the time of the mainnet launch on January 29, 2026, this mechanism remained in the design and discussion phase, without a clearly implemented module at the mainnet contract layer.
● In the absence of a mature validation registry, the reputation of AI agents still primarily relies on raw interaction data and the interpretive logic constructed by various parties. Task publishers, analytical tools, and upper-layer DApps can each define how to score and penalize, but the market lacks a widely recognized "reputation weighting mechanism" at the consensus level. This fragmentation leaves considerable room for score manipulation, witch attacks, and false interactions—provided the costs are low enough, malicious entities can create "glamorous histories" through self-feeding and mutual task brushing.
● Once some form of validation registry is launched on the mainnet, it may serve multiple functions: managing access for validators (who is qualified to issue "completion proofs" or risk control evaluations), weighting agent reputations (agents affirmed by more high-reputation validators gain additional weight), and on-chain marking of malicious behavior (for example, recording multiple defaults, fraud, or associations with known attack events). This effectively inserts a "trust intermediary layer" driven by protocols or governance between raw data and upper-layer applications.
● However, this brings a series of open questions: who will become validators, how to allocate and constrain validation rights, how to avoid concentration of validation rights in a few institutions, and whether the validation registry will undermine the neutrality and censorship resistance claimed by ERC-8004 in the face of political or regulatory pressure. If the validator system becomes overly centralized, the agent economy may be effectively controlled by a few "reviewing institutions"; if governance is too decentralized, it will be challenging to form a consensus that imposes constraints on attackers. These unresolved dynamics will largely determine the fate of ERC-8004 as it evolves from a "technical standard" to "institutional infrastructure."
The Imagination Space and Real Friction of the Trustless Service Market
● In the ideal blueprint, ERC-8004, as a foundation for identity and reputation, can support a trustless service market led by AI agents: on-chain tasks are automatically published and settled through smart contracts, agents self-bid based on historical performance and current load, and payments are automatically released based on verifiable results after task completion; more granular microservices are billed based on call frequency or success rates, with multiple agents combining through standard interfaces to form complex workflows, from data collection to model inference to result interpretation, all orchestrated by a group of "settleable" agents.
● However, compared to its positioning as a "key technological milestone," the real ecosystem is still in a very early exploratory phase. The standard has already landed on the mainnet on January 29, 2026, but the real business needs, business models, and compliance paths surrounding it have yet to be defined. Many so-called "agent market" experiments remain at the hackathon, testnet task, and conceptual demo stages, with a clear disconnect from sustainable revenue streams and stable demand. Currently, ERC-8004 is more of a "reserved interface" rather than a proven successful commercial agreement.
● In this new market construction, developers, infrastructure providers, and task demanders each play different roles and engage in various games. Developers need a unified identity and reputation standard to avoid redundant integration for each platform; infrastructure providers hope to find sustainable charging or token incentive models in areas such as agent registration, task matching, and reputation aggregation; while the true task demanders—whether DeFi protocols, data providers, or enterprise systems—are more concerned with how to obtain comparable and auditable service capabilities without being locked into a single platform. For all three, a unified identity and reputation layer serves as their "common language" for exchange.
● In the short term, resistance is also clearly visible: the learning and education costs of the standard are not low. Transitioning from traditional AI toolchains to the "settleable agent" paradigm requires development teams to reconstruct their architecture and mental models; integrating with existing AI stacks (model hosting, data pipelines, inference services) also involves engineering complexity and performance trade-offs; additionally, the regulatory attitudes of various countries towards the "programmable AI economy" are highly uncertain, and automated execution contracts and autonomous agents may touch on gray areas regarding responsibility allocation, data compliance, and financial attributes. These real-world frictions determine that even if the technical path has been opened, large-scale adoption will not be an overnight story.
From Genesis Month on Mainnet to Long-term Games: The Starting Point of Trust Order for AI Agents
The ERC-8004 standard landed on the Ethereum mainnet on January 29, 2026, not only as an addition of a contract address but also, in terms of time and narrative, declaring that Ethereum has reserved a native seat for the AI agent economy. From the standard's advancement led by Davide Crapis to the "8004 Genesis Month" as an early ecological rallying call, and the discussions surrounding identity, reputation, and the validation registry, Ethereum attempts to pull AI agents from the margins of plugins into the narrative core of public infrastructure on the mainnet.
If we were to give a relatively restrained judgment on this mainnet deployment, it would be: identity and reputation are indeed necessary underlying facilities for the AI agent economy to transition from "toy experiments" to a "settleable market," and ERC-8004 provides a candidate solution that balances openness and standardization. However, between the candidate solution and the actual standard lies a long-term game regarding governance structure, validation system, business model, and regulatory framework. Technical standards can be written and deployed within months, while trust orders can only be slowly generated through real transactions and conflicts.
In the upcoming phases, key signals worth observing include: whether the registration volume and real activity of agents on the mainnet continue to grow, and whether these registrations translate into settleable tasks and revenue; the direction of validation registry-related proposals in community discussions and governance processes, whether it will be implemented as a strongly subjective "whitelist," a multi-party governance weighting system, or replaced by a more decentralized mechanism; and the speed and scope of multi-chain support transitioning from test networks to production environments, which will directly affect whether the AI agent market forms cross-domain unified liquidity or is fragmented into several local segments by different chains and platforms.
More importantly, when interpreting all "scale" data related to ERC-8004, it is essential to maintain a "to be verified" perspective— the figure of "over 10,000 agents registered" during the testnet phase currently comes from a single information source and lacks multi-channel confirmation; various claims regarding ecosystem scale, the number of integration projects, and cross-chain deployment plans also require time and on-chain data for correction. Rather than viewing this mainnet deployment as a settled revolution, it is better to see it as the beginning of a long-cycle story: the trust gamble has just opened, and the true outcomes will slowly emerge through countless task allocations, reputation accumulations, and governance conflicts.
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