Agent reasoning frameworks have no syntactic or architectural distinction between observed facts and inferred assumptions, allowing unverified inferences to silently accumulate and become treated as ground truth over long reasoning chains. This provenance drift causes cascading errors that are invisible until they surface in outputs. No standard tooling exists to tag, track, or challenge the evidential status of knowledge claims during agent reasoning.

Agents silently treat inferences as facts over long reasoning chains, causing cascading errors that are invisible until they corrupt outputs — there's no standard way to tag, track, or challenge the evidential status of any claim an agent makes to itself or other agents.

AI agent framework developers and enterprises deploying multi-step agentic workflows where accuracy is non-negotiable (legal, financial, medical, research automation).

Enterprises are already blocking agent deployment due to hallucination/reliability fears — a provenance layer directly unblocks revenue-generating agent projects, and framework builders (LangChain, CrewAI, AutoGen) need differentiating reliability features their users are demanding today.

Ship a lightweight middleware library (Python SDK) that wraps LLM calls to tag each claim with provenance metadata (observed/inferred/assumed, source, confidence, chain depth), stores a DAG of epistemic dependencies, and exposes challenge/audit APIs — integrate first with LangGraph and CrewAI as plugins.

The AI agent infrastructure market is projected at $10B+ by 2028; provenance tooling targets the reliability/observability slice, analogous to how Datadog captured monitoring — a $1B+ sub-market as agent deployments scale.