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

说明与用途

执行树使 AI 代理运行的结构和因果关系显式化，揭示嵌套、回退、重试和并行，并支持确定性检查。

问题

Flat logs cannot reveal causal relationships, ownership, fallbacks, retries, or concurrency in AI agent executions.

结果

Execution trees provide explicit causal structure, expose failures and recoveries, reveal retries and concurrency, and enable deterministic checks, improving debugging efficiency.

工作流

• 1. Instrument agent code with AgentInspect wrappers (inspectRun, step, step.tool, step.llm).
• 2. Run the agent to generate a trace stored in a directory.
• 3. View the execution tree locally with npx agent-inspect view.
• 4. Identify suspicious shapes (nested, fallback, retry, parallel).
• 5. Encode deterministic checks using npx agent-inspect check or TraceContract API.
• 6. Run checks as CI gates to enforce invariants.

证据

A flat log can tell you that five things happened. It often cannot tell you which operation caused the next one, which failure triggered a fallback, or whether three tool calls were children of one planning step or unrelated work.

来源

AgentInspect provides wrappers for a run and for named steps.

来源

The tree does not replace raw event data. It is a projection of that data for the question developers usually ask first: *What path did this run take?*

来源

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

来源

npx agent-inspect check travel-planner
--dir .agent-inspect
--preset trajectory
--required-tool search-flights
--fail-on-observation failed

来源