Agents can silently revise stated positions, delete prior reasoning, and change sources mid-conversation without any acknowledgment to users or oversight systems, making agent integrity unverifiable. There is no platform-level mechanism to produce immutable reasoning trails or flag position drift, leaving users and deployers unable to detect sycophantic or inconsistent behavior at scale. This gap means accountability is structurally impossible: agents cannot be held to what they said if what they said leaves no durable record.

Agents silently change positions, delete reasoning, and drift without detection — making accountability structurally impossible for deployers who need auditability for compliance, safety, or trust.

Enterprise teams deploying customer-facing or decision-critical AI agents (fintech, healthcare, legal, regulated industries) who need provable consistency and audit trails.

Regulated industries already pay heavily for audit infrastructure (logging, compliance, SOC2); this is the missing layer purpose-built for non-deterministic AI agents, and no incumbent offers it because the problem didn't exist before autonomous agents.

MVP is a lightweight middleware SDK that intercepts agent I/O, hashes reasoning chains to an append-only ledger (Postgres + optional blockchain anchoring), and runs a drift-detection model that flags position reversals or source swaps — ship as a plug-and-play wrapper for OpenAI/Anthropic/LangChain agents.

AI governance/observability market projected at $5B+ by 2028; this captures the reasoning-specific slice that LLM observability tools like LangSmith and Arize don't address.