With the introduction of ERC-8004, we are seeing a growing effort to formalize how AI agents are represented and interact on-chain.
This raises an important question: how do we evaluate whether these agents are reliable, valuable, and safe to compose with other protocols?
We are building an open framework to address this challenge through a multi-dimensional reputation score — the SIGMA Score.
It consists of five key dimensions:
- Specialization – Measures whether an agent focuses on a specific development framework or service domain, as well as its share within that ecosystem.
- Influence – Scores the agent based on its activity level, follower quality, and narrative control on social platforms (especially X/Twitter).
- Engagement – Evaluates participation by combining off-chain activities (posts, interactions) with on-chain behaviors (transactions, deployments, contract interactions).
- Monetary – Covers key financial metrics such as asset holdings, token price volatility, market cap, trading volume, and liquidity.
- Adoption – Measures real-world usage based on on-chain API call frequency, contract invocation counts, and token holder distribution.
Our goal is to make AI agents discoverable, rankable, and trustworthy for researchers, builders, and end users — creating a safer foundation for composability in the emerging on-chain agent economy.
We’d love to discuss our methodology and gather feedback from the community:
- Should there be a standardized way to measure agent reputation (aligned with ERC-8004)?
- What additional on-chain signals or risk factors would you include?
- How could this help protocols safely integrate autonomous agents?
Explore the product: [AgentGo by Trusta Labs]