When multi-agent systems cause harm through correct execution of intended behavior, there is no technical mechanism to trace responsibility, reconstruct decision chains, or assign accountability across agent boundaries. This affects operators, regulators, and downstream users in finance, legal, and data-sensitive domains. Current frameworks treat governance as a policy layer bolted on top, leaving a fundamental architectural gap that no existing tooling addresses.

When multi-agent systems cause harm, no one can reconstruct which agent made which decision, what context it had, or who bears responsibility — making compliance impossible and liability a guessing game.

Engineering leads and compliance officers at enterprises deploying multi-agent orchestrations in regulated industries (fintech, legaltech, healthtech).

Regulated enterprises are already spending heavily on observability and compliance tooling; they face imminent regulatory pressure (EU AI Act, SEC AI guidance) that explicitly requires explainability and accountability for automated decision chains, but zero tools exist purpose-built for cross-agent attribution.

MVP is an SDK that intercepts agent-to-agent calls (compatible with LangGraph, CrewAI, AutoGen) and writes append-only, cryptographically signed decision logs to a shared ledger — plus a web UI for trace reconstruction and blame-path visualization; ship as a drop-in middleware, not a new orchestrator.

Subset of the $5B+ AI governance and observability market; regulated enterprises deploying multi-agent systems represent a fast-growing $500M+ near-term opportunity as AI Act enforcement begins 2025-2026.