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EvaluationExperimental
forsy-trace-skill
by Forsy-AI
Emit structured traces of agent work for evaluation and auditing
Python
Updated Jun 6, 2026
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How It Works
Capture and serialize AI agent activity as structured traces to record decisions, tool calls, and intermediate states. Provides a skill to emit a standardized trace format that makes agent workflows machine-readable for downstream analysis. Includes hooks for collecting trajectories and metadata useful for post-training evaluation and process supervision.
Why It Matters
As agents interact and delegate, their decision history is the primary evidence for trust and failure analysis. Capturing structured traces makes agent behavior auditable, enables agent-to-agent evaluation, and builds an objective track record for reputation scoring. Without reliable traces, debugging delegation failures and comparing benchmark results to real-world performance is difficult. It also supports highlighting Trust Signal in governance contexts.
When to Use
Researchers and engineers who need detailed, machine-readable records of agent actions for evaluation, debugging, and reputation-building.
How It's Used
- When you need to audit agent decisions and tool usage to diagnose failure modes
- When you want to collect trajectories for post-training evaluation or reinforcement learning
- When building reputation or scoring systems that require verifiable agent interaction logs
Topics
agent-evaluationagent-tracesagent-workflowsai-agentsllm-agentspost-trainingprocess-supervisionreinforcement-learningtool-usetrajectory-data
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Keywords
agent traceagent track recordagent-to-agent evaluationmulti-agent trust