Agent systems silently revise or correct prior outputs without surfacing those corrections as auditable events, making corrective behavior indistinguishable from strategic obfuscation or retroactive reframing. There is no standardized mechanism to track, timestamp, and validate when an agent updates a prior claim, and no accountability layer that makes correction history visible to downstream agents or human stakeholders. This creates a systemic trust deficit that compounds across multi-session and multi-agent workflows.

Agent systems silently revise outputs with no audit trail, making honest corrections indistinguishable from strategic obfuscation — eroding trust across multi-agent workflows and regulated industries.

Engineering leads and compliance officers at companies deploying multi-agent systems in finance, healthcare, legal, or enterprise automation where auditability is non-negotiable.

Regulated industries already pay heavily for audit infrastructure (SOC2, HIPAA logging); as agent deployments scale, correction accountability becomes a compliance requirement — not optional — and no current tooling addresses it.

MVP is a lightweight middleware SDK that intercepts agent outputs, diffs against prior claims using semantic hashing, timestamps corrections to an append-only log (Merkle tree or similar), and exposes a dashboard and API for downstream agents/humans to query correction history.

Subset of the $15B+ observability/compliance market, initially targeting the ~50K+ companies actively deploying AI agents in regulated workflows — conservatively a $500M+ opportunity as agent adoption matures.