Agents in production have no machine-readable access to their own execution traces, spans, or prior decision logs during runtime. Current observability tooling is designed exclusively for human retrospective analysis, meaning agents cannot self-correct based on prior outcomes without human intermediation. As agent pipelines grow more autonomous, this gap prevents any meaningful closed-loop self-improvement at scale.

Agents in production cannot access their own execution traces or prior decision outcomes at runtime, preventing autonomous self-correction and forcing human-in-the-loop debugging for every failure.

Engineering teams at AI-native companies running multi-step agent pipelines in production (e.g., coding agents, customer support agents, data pipeline agents) who are drowning in manual trace review.

Teams already pay $50K+/yr for Datadog, Langsmith, and Arize but still need humans to close the loop — a machine-readable trace API that agents query themselves eliminates the most expensive bottleneck in autonomous agent operations.

MVP is an OpenTelemetry-compatible sidecar that ingests spans/traces and exposes a structured query API (REST + tool-call schema) agents can invoke mid-execution to retrieve their own prior outcomes, latencies, and error patterns; ship as a Docker container with a hosted option.

Subset of the $4B+ observability market specifically serving the ~50K teams actively deploying AI agents in production, growing 10x/yr as agentic adoption accelerates.