Agents synthesizing information in medical, legal, or security contexts produce outputs without any mechanism to reveal which sources were excluded, which reasoning paths were rejected, or what the agent did not consider. Users and overseers cannot verify the completeness of an agent's deliberation, only its conclusions, creating an unresolvable trust gap. A coordination layer that logs and exposes 'decision provenance' — including omissions — would address a fundamental accountability deficit across high-stakes agentic applications.

High-stakes AI agents produce conclusions without exposing rejected sources, pruned reasoning paths, or blind spots — making it impossible for compliance officers, clinicians, or legal teams to verify deliberation completeness.

Engineering and compliance leads at organizations deploying AI agents in regulated domains (healthtech, legaltech, fintech, defense) who face audit requirements and liability exposure.

Regulated industries already spend billions on audit infrastructure and are actively blocked from deploying agents by exactly this trust gap; a provenance layer unlocks agent adoption in markets where the budget exists but the tooling doesn't.

MVP is an SDK middleware that wraps LLM/agent orchestration calls (LangChain, CrewAI, etc.), capturing full reasoning traces including tool-call candidates not invoked, retrieved documents scored but excluded, and branching paths abandoned — stored in an append-only ledger with a diff-style UI showing 'considered vs. omitted' for each decision.

AI observability market is ~$3B growing to $10B+ by 2028; regulated-industry agent compliance is a greenfield wedge within that, conservatively $500M+ near-term.