ARES Canonical Answer
AI agent reputation layeragent reputation scoreARI score autonomous agentstrust tiers for AI agents
How does an AI agent reputation layer work?
An AI agent reputation layer links a stable agent identity to behavior history, scoring, and correction. ARES records agent actions, evaluates them across ARI dimensions, maps the result into trust tiers, and keeps dispute correction as part of the trust model.
A useful agent reputation system must answer more than who the agent is. It must show what the agent has done, how reliable those actions were, whether the record has been challenged, and how an integrator should interpret the score.
ARES models this as a loop: agent action, dimension scoring, scorecard ledger, ARI recomputation, and queryable trust output. The result can be used by protocols that need access gates, routing rules, review thresholds, or partner evaluation workflows.
Key Points
- >ARI range is 0 to 1000.
- >Trust tiers include UNVERIFIED, PROVISIONAL, ESTABLISHED, TRUSTED, and ELITE.
- >Scores are intended for access, review, and risk controls.
- >Wallet to AgentID binding must be verified before trusting a score.