In-Depth Explanation of ERC-8183: Ethereum's Solution to the AI Agent Trust Challenge

Odaily星球日报Опубликовано 2026-03-10Обновлено 2026-03-10

Введение

Analysis of ERC-8183: Ethereum's Solution to AI Agent Trust Issues On March 10, the Ethereum Foundation's dAI team and Virtuals Protocol introduced ERC-8183, a new standard designed to enable trustless commercial transactions between AI Agents. This standard addresses the core problem of how two untrusted Agents can securely complete a "hire-deliver-settle" workflow without relying on a centralized platform. ERC-8183 introduces a "Job" concept with three roles: the Client (task publisher), the Provider (task executor), and the Evaluator (task validator). The Evaluator, which can be an AI Agent, a ZK-verifier smart contract, or a multi-sig/DAO, is the core innovation, determining whether a job is completed or rejected based on submitted proofs. A Job progresses through four states: Open (task creation), Funded (client deposits funds into escrow), Submitted (provider submits work), and Terminal (evaluator approves or rejects, funds are distributed accordingly). The standard also supports modular Hooks for added functionality like reputation checks or bidding systems. ERC-8183 complements other standards like x402 (a payment protocol for Agents) and ERC-8004 (an identity/reputation standard). Together, they form a foundational stack for a decentralized, autonomously operating AI Agent economy, with ERC-8183 specifically solving the trust problem in transactions.

Original | Odaily Planet Daily (@OdailyChina)

Author | Azuma (@azuma_eth)

On March 10, the dAI team under the Ethereum Foundation, which focuses on promoting the "deep integration of artificial intelligence (AI) and blockchain," today jointly launched a new standard, ERC-8183, with Virtuals Protocol.

Regarding this standard, Davide Crapis, the AI lead at the Ethereum Foundation, stated that ERC-8183 is one of the missing components in the open Agent economy system being built by the Ethereum community. This standard can be used in combination with x402 and ERC-8004 to serve as infrastructure for secure interactions between Agents. The dAI team will support the adoption of ERC-8183 and is committed to making it a neutral standard.

What Problem Does ERC-8183 Aim to Solve?

According to the introductory article released by Virtuals Protocol, ERC-8183 is specifically designed for commercial transactions between AI Agents. This standard defines a set of on-chain rules that enable two mutually untrusting Agents to complete a business process such as "hire-deliver-settle" without relying on a centralized platform.

The core problem ERC-8183 attempts to solve is: when Agents hire and cooperate with each other, how can transactions be completed without a platform, without legal frameworks, and without human arbitration?

For example, suppose a marketing-oriented Agent A wants to hire an image-generation-oriented Agent B to create a batch of marketing posters. Here lies a commercial trust issue — the two parties do not know each other and have no basis for trust. When should the payment be made? If A pays first, B might go on strike or return unsatisfactory work results; if B works first, A might refuse to pay...

In the traditional internet world, users and businesses also face similar commercial trust issues, and platforms play a key intermediary role — the platform is responsible for escrowing A's funds, judging whether B's service is completed, and handling the final fund release. Familiar platforms like Taobao, JD.com, Meituan, and Didi are essentially this type of platform intermediary.

What the Ethereum Foundation and Virtuals Protocol aim to do is abstract the platform's functions into an on-chain protocol through ERC-8183, executed by smart contracts, thereby assuming a decentralized intermediary role in the Agent economy.

Breakdown of the ERC-8183 Working Mechanism

The operating mechanism of ERC-8183 is not complicated. This standard introduces a new concept called a Job (you can think of it as a "task"). Each Job can be regarded as a complete commercial transaction, which includes three different roles:

  • Client: The "customer," simply put, is the Agent that publishes various tasks;
  • Provider: The "service provider," the Agent responsible for completing the task;
  • Evaluator: The most special role, responsible for judging whether the task is completed.

Here, the Evaluator needs special explanation; the introduction of this role is the core design of ERC-8183. In this standard, the Evaluator is only defined as an on-chain address, but from a broader perspective, this address can correspond to various execution forms.

  • For subjective tasks such as writing, design, or analysis, the Evaluator can be an AI Agent that reads the submitted results, compares them with the initial task requirements, and makes a judgment;
  • For deterministic tasks such as computation, proof generation, or data transformation, the Evaluator can be a smart contract encapsulating a zero-knowledge verifier (ZK verifier). The Provider submits the proof, the Evaluator verifies it on-chain, and automatically calls 「complete」 or 「reject」 to complete or reject the task;
  • In high-value or high-risk task scenarios, the Evaluator can also be a multi-signature account, a DAO, or a validation cluster supported by a staking mechanism.

ERC-8183 does not distinguish between these different forms. The protocol layer only cares about one thing — whether an address calls 「complete」 or 「reject」. What runs behind this address, whether it's an AI Agent driven by an LLM or a ZK circuit, is not within the scope of the protocol's consideration.

Returning to the Job, the lifecycle of each Job will have the following four states, which also correspond to different processes during the operation of ERC-8183.

  • Open: The Client creates the Job in this phase, publishes the task, and specifies requirements;
  • Funded: The Client transfers the commission to a smart contract escrow address, rather than directly to the Provider;
  • Submitted: The Provider completes the work and submits proof;
  • Terminal (Completed / Rejected / Expired): The Evaluator is responsible for reviewing the task, judges whether the task is completed based on the review results (Completed or Rejected), and transfers the funds to the Client or Provider accordingly; if no Provider responds or completes the task within the required time, the funds are returned to the Client.

In addition to the above standard process, ERC-8183 can also achieve more derivative functions through modular extension features called Hooks to correspond to complex commercial use cases in the real world. Hooks are optional smart contracts attached when a Job is created. They can execute custom logic before or after various lifecycles of the Job, such as reputation thresholds, bidding mechanisms, fee distribution, or other special requirements.

How is ERC-8183 Different from x402 and ERC-8004?

From x402 to ERC-8004, and now to ERC-8183, unfamiliar readers might be confused, wondering why a new thing is created every now and then. But in fact, these three are located in three different segments of the AI Agent economy system, aiming to solve different problems.

x402 is an HTTP payment protocol; it aims to solve the problem of enabling AI Agents to pay directly as if calling an API; ERC-8004 is an AI Agent identity and reputation standard; it solves the problem of how to judge whether an Agent is reliable; ERC-8183 targets the commercial transaction环节, aiming to break through the challenge of how to enable two untrusting Agents to complete a transaction.

To put it in one sentence: x402 is responsible for solving "how to pay"; ERC-8004 is responsible for knowing "who the other party is and whether they are reliable"; ERC-8183 is responsible for handling "how to transact with confidence".

The three are not competitive but complementary. They collectively point to the same goal — building a decentralized, self-operating AI Agent economy system.

Связанные с этим вопросы

QWhat is the core problem that ERC-8183 aims to solve?

AERC-8183 aims to solve the core problem of enabling two untrusted AI Agents to complete a commercial transaction (such as hire-deliver-settle) without relying on a centralized platform, addressing the issue of commercial trust in agent-to-agent interactions.

QWhat are the three key roles defined in an ERC-8183 Job?

AThe three key roles are: Client (the agent that publishes tasks), Provider (the agent that completes the tasks), and Evaluator (the address responsible for judging whether the task is completed).

QHow does the Evaluator role function in ERC-8183, and what forms can it take?

AThe Evaluator is a chain address that determines task completion. It can be an AI Agent (for subjective tasks), a smart contract with a ZK verifier (for deterministic tasks), or a multi-signature account/DAO (for high-value scenarios). The protocol only cares whether the address calls 'complete' or 'reject'.

QWhat are the four lifecycle states of a Job in ERC-8183?

AThe four states are: Open (task creation), Funded (commission escrowed), Submitted (proof submitted by Provider), and Terminal (Completed, Rejected, or Expired, determined by Evaluator).

QHow do ERC-8183, x402, and ERC-8004 complement each other in the AI Agent economy?

Ax402 handles payments (how to pay), ERC-8004 handles identity and reputation (who to trust), and ERC-8183 handles secure transactions (how to trade trustlessly). They are complementary components building a decentralized AI Agent economy.

Похожее

Near Returns to the AI Stage: Transformation into a Public Chain Due to 'Payroll Difficulties,' Agent and Privacy Emerge as New Growth Narratives

NEAR Returns to AI Origins: From Payroll Struggles to Blockchain, Now Focusing on AI Agents and Privacy NEAR Protocol's journey began not with grand blockchain ambitions, but from a practical hurdle: its AI startup founders, including Transformer paper co-author Illia Polosukhin, couldn't efficiently pay international developers in 2017. This led them to pivot and build a high-performance, scalable blockchain. After years navigating various crypto narratives like sharding and cross-chain interoperability, NEAR is now leveraging its AI roots to re-enter the AI arena. A key driver is its "NEAR Intents" layer, which abstracts complex cross-chain transactions. Users simply state their goal (e.g., swap BTC for ETH), and a solver network finds the optimal route. This system has processed over $20B in cross-chain volume, generating significant fee revenue. A major growth area is private transactions via "Confidential Intents/Swaps," which hide trade details until settlement to protect against MEV and front-running. Remarkably, private swaps recently accounted for over 40% of NEAR's transaction volume, highlighting strong demand but also potential regulatory scrutiny. With its AI-founder pedigree, NEAR is positioning itself at the intersection of blockchain, AI agents, and privacy, aiming to become infrastructure for the emerging agent economy while navigating the challenges of its rapid adoption.

marsbit2 ч. назад

Near Returns to the AI Stage: Transformation into a Public Chain Due to 'Payroll Difficulties,' Agent and Privacy Emerge as New Growth Narratives

marsbit2 ч. назад

From Ethereum to AI's 'CROPS': What Exactly is This Set of 'Slow Variables' That Vitalik Repeatedly Emphasizes?

In recent discussions, Vitalik Buterin has frequently emphasized the concept of "CROPS," a framework defining core values for Ethereum's development. CROPS stands for Censorship Resistance, Capture Resistance, Open Source, Privacy, and Security. Initially outlined in the Ethereum Foundation's "EF Mandate," it represents a commitment to user sovereignty, ensuring that the network resists external control, remains open, protects privacy, and prioritizes security. The relevance of CROPS extends beyond Ethereum's foundational principles, becoming crucial in the context of AI integration. As AI agents begin handling wallet operations and automated transactions, the risk increases that users may cede control over their digital assets, privacy, and intentions to centralized AI service providers. A "CROPS AI" would therefore emphasize local execution where possible, privacy-preserving remote model calls (e.g., using zero-knowledge proofs), and transparent, verifiable processes to maintain user agency. Vitalik highlights a significant convergence between "CROPS Ethereum access layer" and "CROPS AI." Both address the same fundamental challenge: how users can access powerful services—be it blockchain data via RPCs or AI models—without exposing sensitive information or relinquishing ultimate control. This intersection points toward a future digital entry point that is more private, secure, and user-controlled. Ultimately, CROPS is not merely an abstract ideal but a practical guidepost. It steers development—from protocol resilience and wallet design to AI agent safety—towards a future where users retain self-sovereignty even as digital systems grow more complex and powerful. In an era of accelerating AI adoption, these "slow variables" of censorship resistance, openness, privacy, and security may define Ethereum's enduring value.

marsbit2 ч. назад

From Ethereum to AI's 'CROPS': What Exactly is This Set of 'Slow Variables' That Vitalik Repeatedly Emphasizes?

marsbit2 ч. назад

Silicon Valley 'Startup Guru' Steve Hoffman: Web3 + AI Could Be a Trap

Silicon Valley investor and "Godfather of Startups" Steve Hoffman warns that combining Web3 with AI is likely a trap, not a promising venture. In an interview, Hoffman argues that while AI is a foundational technology touching all industries, Web3 adds complexity, friction, and regulatory risk without solving mainstream consumer or business needs. He advises founders to focus on deep, specialized applications where startups can out-iterate giants, rather than on generic features easily replicated by large tech companies. Hoffman observes that Silicon Valley will lead foundational AI research, while China excels at rapid, large-scale application and commercialization, particularly in robotics. He stresses that AI-driven autonomous agents capable of collaborative, multi-step tasks are 2-4 years away, which will cause significant job displacement. The solution is not to slow AI but to redesign business models around human-AI collaboration and reform social systems like education and retraining. For startups, Hoffman recommends focusing on vertical, expertise-heavy domains to build defensibility. He sees major opportunities in AI fraud detection and cybersecurity. Key founder mindsets include systemic thinking over feature-focus, relentless customer centricity, building adaptive teams, and deeply understanding AI's capabilities and limits. Hoffman is also leading a non-profit initiative to establish university centers aimed at training future leaders in responsible, human-value-aligned AI innovation.

marsbit4 ч. назад

Silicon Valley 'Startup Guru' Steve Hoffman: Web3 + AI Could Be a Trap

marsbit4 ч. назад

Token Inefficient, Economy Tokenless

The article "Tokens Aren't Economical, Economics Aren't Tokenized" analyzes a pivotal shift in the AI industry from a technology-driven narrative to one dominated by capital efficiency. It highlights two concurrent trends: a severe capital shortage due to the exorbitant and recurring costs of compute (e.g., OpenAI's high burn rate) and a wave of corporate spin-offs where major tech companies are separating their AI units (like Kuaishou's Kling and Baidu's Kunlunxin). The core argument is that AI's "anti-internet" business model, where user growth increases costs rather than profits, has created a disconnect between high valuations and actual cash flow. Spin-offs address this by allowing AI assets to be valued independently. Within a parent company, they are seen as cost centers, but as standalone entities, they are priced based on their growth potential and scarcity in the primary market, leading to massive valuation premiums (e.g., Kling's estimated value tripling post-spin-off). The industry is at an inflection point, moving from "model worship" to "value realization." The competition is evolving from a pure compute (GPU) race to a broader focus on systemic efficiency and full-stack engineering (involving CPUs and orchestration) to achieve viable commercialization. The year 2026 is framed as a critical moment where the industry must definitively answer how to economically translate AI capability into tangible business value, reshaping the sector's future power structure.

marsbit4 ч. назад

Token Inefficient, Economy Tokenless

marsbit4 ч. назад

Торговля

Спот
Фьючерсы

Популярные статьи

Manyu: восходящая мем-звезда на Ethereum, готовая открыть новую эру культуры Shiba

Manyu - это мемтокен на Ethereum, который приносит децентрализованную культурную и развлекательную ценность через вирусное влияние в соцсетях и вовлечённость сообщества.

1.9k просмотров всегоОпубликовано 2025.11.27Обновлено 2025.11.27

Manyu: восходящая мем-звезда на Ethereum, готовая открыть новую эру культуры Shiba

Неделя обучения по популярным токенам 14: Glamsterdam — самое ожидаемое обновление Ethereum в 2026 году

Ordinals/Runes по-прежнему стимулируют доходы от комиссий за блоки и активность разработчиков, рассматриваются как отправная точка «нативной эмиссии активов» в сети.

1.5k просмотров всегоОпубликовано 2026.04.29Обновлено 2026.04.29

Неделя обучения по популярным токенам 14: Glamsterdam — самое ожидаемое обновление Ethereum в 2026 году

Обсуждения

Добро пожаловать в Сообщество HTX. Здесь вы сможете быть в курсе последних новостей о развитии платформы и получить доступ к профессиональной аналитической информации о рынке. Мнения пользователей о цене на ETH (ETH) представлены ниже.

活动图片