When an agentic system fails, the problem is often larger than a single bad response. A handoff may happen too late, a specialist agent may miss the same signal across many runs, or a review process may trigger for the wrong class of cases. To improve the system, teams need to see recurring behavior across the whole population of traces.
This cookbook walks through a macro-eval workflow for a multi-agent system. We use a synthetic EV order workflow where specialist agents handle pricing, compliance, supply, factory routing, scheduling, and release decisions while market and operational conditions change.
Intro | Promptfoo
romptfoo is an open-source CLI and library for evaluating and red-teaming LLM apps.
With promptfoo, you can:
- Build reliable prompts, models, and RAGs with benchmarks specific to your use-case
- Secure your apps with automated red teaming and pentesting
- Speed up evaluations with caching, concurrency, and live reloading
- Score outputs automatically by defining metrics
- Use as a CLI, library, or in CI/CD
- Use OpenAI, Anthropic, Azure, Google, HuggingFace, open-source models like Llama, or integrate custom API providers for any LLM API
The goal: test-driven LLM development, not trial-and-error.
