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

说明与用途

用执行树替代平面日志，显式显示操作层级、失败路径、重试和并发，加速 AI 代理的调试。

问题

Flat logs lack explicit causality, making it hard to understand which operations caused others, detect failures, retries, or concurrency in AI agent runs.

结果

Using execution trees, developers can quickly identify ownership, fallback usage, retries, and parallelism, leading to faster bug detection and more reliable agent behavior.

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 to inspect paths, failures, retries, and concurrency.
• 4. Identify patterns (e.g., retries, fallbacks) that indicate bugs or performance issues.
• 5. Encode deterministic checks using agent-inspect check or TraceContract API to enforce invariants (e.g., max retries).
• 6. Integrate checks into CI to fail on violations.

证据

An execution tree makes the relationship explicit: support-agent ├── plan ├── fetch-inventory (failed: 503) ├── fetch-inventory (success) └── draft-answer

来源

The tree preserves both facts: the primary path failed; the recovery path completed.

来源