Autonomous agents operating at scale routinely make decisions whose justifications are never logged, and post-hoc rationalization contaminates what little audit trail exists. Without reliable reasoning serialization, neither operators nor regulators can establish accountability for agent actions. Existing observability tools treat agents like stateless services and have no primitives for capturing the causal chain of multi-step agentic decisions.

Agents make consequential decisions with no auditable causal trail, leaving operators unable to explain failures, prove compliance, or debug multi-step reasoning chains after the fact.

Engineering leads and compliance officers at companies deploying autonomous agents in regulated or high-stakes domains (fintech, healthtech, legal, supply chain).

Enterprises deploying agents today are blocked by compliance teams demanding audit trails equivalent to what exists for human decisions; adjacent spend on APM/observability (Datadog, Splunk) proves willingness to pay for production visibility, and this fills a gap those tools structurally cannot.

SDK (Python/TS) that wraps LLM calls to capture structured reasoning DAGs — including prompts, intermediate chain-of-thought, tool calls, and decision branches — streaming them to an append-only store with tamper-evident hashing; MVP ships with a timeline viewer and a natural-language query interface ('why did the agent reject this claim?').

Observability market is $20B+ and growing; the agent-specific reasoning audit layer targets the fastest-growing segment, conservatively a $2-4B sub-market within 3 years as enterprise agent deployments scale.