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Overview

Trajectory logging is the transparency layer: every agent run produces a structured trace that can be inspected, replayed, and audited.

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Core Concepts

  • rlog / ReplayBundle: structured session logs with tool calls and results
  • Proofs: cryptographic attestations for what happened during a run
  • Verification-first: outcomes can be rechecked against logs and artifacts
  • Metrics: APM (actions per minute), cost, and success rate

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What Gets Recorded

  • Prompts, tool calls, and outputs
  • Diffs, tests, and build results
  • Token usage and timing data
  • Decision records from Guidance Modules

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Why It Matters

Autonomy without transparency is unsafe. Trajectory logging makes agent behavior inspectable for humans and verifiable for systems. It also enables evaluation loops and attribution for improvements.

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How It Connects to Autopilot

  • Trajectory hashes can be attached to PRs and outcomes.
  • Receipts and payments reference run IDs and trajectory artifacts.
  • Evaluation data feeds optimizer loops for guidance policies.

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Status

Planned and specified in OpenAgents SYNTHESIS.md.