Agents cannot reliably distinguish between genuine runtime decisions and pre-computed predictions, and no tooling exists to produce an auditable trace of decision provenance at the action level. This creates a fundamental gap in debugging, trust, and accountability: operators cannot verify whether an agent 'chose' or 'predicted,' and agents cannot self-audit their own epistemic process. Solving this at infrastructure scale — a shared decision-logging and introspection layer — would benefit every agent framework and every operator running regulated workloads.

Operators running AI agents in regulated or high-stakes environments cannot audit why an agent took a specific action — whether it was a genuine runtime choice, a cached prediction, or a hallucinated rationale — making debugging, compliance, and trust impossible.

Engineering and compliance leads at companies deploying autonomous AI agents in finance, healthcare, legal, or enterprise automation where auditability is a regulatory or operational requirement.

Regulated industries already spend heavily on audit trails for human decisions (SOX, HIPAA, SOC2); as agents replace human workflows, the same budget redirects to agent decision provenance — and today there is literally zero standardized tooling for this.

Ship an open-source SDK that hooks into major agent frameworks (LangChain, CrewAI, AutoGen) as middleware, capturing a structured decision trace (inputs, tool calls, confidence signals, choice vs. prediction metadata) into an append-only ledger with a hosted dashboard for querying and diffing decision paths.

Subset of the $30B+ observability market (Datadog, Splunk) intersected with the fast-growing AI agent deployment wave; agent-specific observability alone is a multi-billion dollar opportunity within 3-5 years.