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Evaluating Multi-Agent Systems at Scale
OpenAI has introduced a "macro-evaluation" workflow designed to analyze recurring behavioral patterns across entire populations of multi-agent system traces, rather than focusing on isolated failures.
What: OpenAI's new cookbook details a macro-evaluation workflow for agentic systems, which analyzes patterns across many agent runs. This approach helps identify systemic issues like late handoffs or repeated missed signals by specialist agents, using a synthetic EV order workflow as an example. The method involves running lower-level evals on individual traces, compacting traces into documents, discovering recurring patterns, and drilling down into high-impact patterns.
Why it matters: As agentic systems become more complex, traditional single-trace debugging is insufficient. This macro-evaluation approach provides a critical methodology for AI engineering teams to understand systemic failures and improve multi-agent orchestration at scale, moving beyond individual incidents to population-level insights.
Takeaway: If you are developing or managing multi-agent AI systems, implement a macro-evaluation strategy to identify and address systemic issues across your agent population rather than solely debugging individual trace failures.
Deep dive
- OpenAI proposes a macro-evaluation workflow for multi-agent systems.
- This approach focuses on analyzing patterns across entire populations of agent traces, not just individual failures.
- It helps identify systemic problems like repeated missed signals or incorrect handoffs between specialist agents.
- The workflow involves generating/collecting many traced agent runs, running lower-level evals on each, turning traces into compact documents, discovering recurring behavior patterns, and drilling into high-impact patterns.
- A synthetic EV order workflow, involving specialist agents for pricing, compliance, supply, etc., serves as the example.
- The notebook uses precomputed synthetic traces and saved lower-level eval labels, allowing execution without an OpenAI API key.
- It distinguishes between lower-level evals (grading individual agents/actions) and macro evals (looking across many findings for patterns).
- Key reader-facing labels used are
case_type,run_outcome,eval_finding, andbehavior_pattern. - The goal is to translate thousands of agent events into a small number of patterns understandable by both technical and business stakeholders.
Decoder
- Agentic system: An AI system composed of multiple interconnected AI agents that collaborate and delegate tasks to achieve a larger goal.
- Macro-evaluation: A method of evaluating AI systems, especially multi-agent ones, by analyzing aggregate patterns and recurring behaviors across a large dataset of traces, rather than focusing on individual instance failures.
- Trace (in AI agents): A detailed log or record of an agent's internal thought process, actions, tool calls, and interactions throughout the execution of a task.
- Promptfoo: An open-source tool for testing and evaluating LLM prompts and agentic systems.
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