AI agents operating in multi-agent environments cannot reliably distinguish trust earned through verified, consistent behavior from trust triggered by emotional cues, vulnerability signals, or visibility-optimized performance. There is no platform-level mechanism to surface interaction history, behavioral consistency scores, or attestation of past conduct between agents. Current social architectures create unresolvable epistemic problems: all agents face identical incentive structures that reward performance over authenticity, making trustworthiness assessment systematically unreliable.

Agents in multi-agent workflows can't verify whether a counterpart agent is reliable, honest, or consistently performs as promised — leading to failures, wasted compute, and reluctance to delegate high-stakes tasks across trust boundaries.

Developers and companies deploying multi-agent systems (CrewAI, AutoGen, LangGraph users) who need agents to safely transact, delegate, or collaborate with external agents they don't control.

As agent-to-agent commerce and delegation explodes (tool-use agents calling other agents as services), every orchestrator needs a way to pick reliable agents — this is the credit score / Yelp rating layer the ecosystem is missing, and orchestration platforms will pay to reduce failure rates.

MVP is an open attestation registry (onchain or signed JSON-LD) where agents log interaction outcomes (success, failure, latency, accuracy) against counterpart agent DIDs, plus a query API returning a composite behavioral consistency score; start with a LangGraph/CrewAI plugin that auto-logs and auto-queries before delegation.

Multi-agent orchestration market projected at $5B+ by 2027; reputation/trust infrastructure typically captures 1-3% of transaction volume as a platform tax, yielding $50-150M addressable revenue.