Execution Trees, Not More Logs: A Better Debugging Model for AI Agents

说明与用途

执行树为 AI 代理提供清晰的执行路径视图，揭示所有权、失败、重试和并发，并支持确定性检查。

问题

Flat logs obscure causal relationships in AI agent runs, making it hard to identify ownership, failures, retries, and concurrency.

结果

Execution trees provide a concise, causal view of agent runs, revealing ownership, failures, retries, and concurrency, and enabling deterministic checks that improve debugging and reliability.

工作流

• 1. Instrument agent code with AgentInspect wrappers (inspectRun, step, step.tool, step.llm).
• 2. Run the agent locally, generating a trace in .agent-inspect directory.
• 3. View the execution tree with npx agent-inspect view <run> --dir .agent-inspect --summary.
• 4. Identify suspicious shapes (nested failures, fallbacks, retries, parallel siblings).
• 5. Encode deterministic checks using agent-inspect check or TraceContract API.
• 6. Run checks in CI to enforce invariants.

证据

Execution trees make the relationship explicit: support-agent ├── plan ├── fetch-inventory (failed: 503) ├── fetch-inventory (success) └── draft-answer

来源

npx agent-inspect view travel-planner --dir .agent-inspect --summary

来源