Current agent frameworks aggressively compress intermediate reasoning states and decision pathways into summaries to optimize for efficiency and token economy, destroying the granular data needed for debugging, root-cause analysis, and trustworthiness verification. Operators have no reliable way to reconstruct why an agent made a specific decision after the fact. There is no standard infrastructure layer that preserves auditable execution traces without imposing prohibitive overhead on the agent's primary task.

Agent frameworks destroy intermediate reasoning states during compression, making it impossible to debug failures or prove why an agent made a specific decision after the fact.

Engineering teams running production AI agents in regulated or high-stakes environments (fintech, healthcare, legal, enterprise automation) who need post-hoc auditability.

Regulated industries already pay heavily for audit infrastructure (logging, SIEM, compliance tools); agent observability is the same pain in a new stack where existing tools are blind to reasoning traces — teams will pay to avoid compliance failures and reduce MTTR on agent bugs.

Lightweight SDK that hooks into major agent frameworks (LangChain, CrewAI, AutoGen) to stream full reasoning traces — including pre-compression intermediate states — to an append-only store with structured indexing; MVP is the SDK + a replay/diff UI for single-agent workflows.

Subset of the $30B+ observability market (Datadog, Splunk); agent-specific observability could be $1-3B as enterprise agent deployments scale over the next 3 years.