#AI Agents
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EvaluationDeepEval vs Vally vs Waza vs convo-eval: A Technical Decision Guide
Match the evaluator to the evidence boundary: DeepEval for responses and traces, Vally for trajectories and workspaces, Waza for skills, and a licensed voice harness for deployed audio.
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EvaluationDeepEval: Pytest-Native Evaluation for LLM Applications and Agents
DeepEval is the strongest general-purpose starting point for Python LLM, RAG, and agent evaluation. Start with deterministic outcomes, then add calibrated semantic metrics where code falls short.
Artwork: Wheat Field with Cypresses by Vincent van Gogh. The Metropolitan Museum of Art · Public domain
EvaluationMicrosoft Vally: Declarative Trajectory Evaluation for AI Agents
Vally offers the strongest trajectory pipeline for coding and tool-using agents, but its pre-1.0 contracts and unresolved source and support model make a version-pinned pilot safer than an organization-wide gate.
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EvaluationMicrosoft Waza: Evaluation for Agent Skills, Triggers, and Tool Behavior
Waza is a strong specialist for testing skill selection, anti-triggers, tools, and workspace effects. Pair it with other evaluators for RAG, voice, multimodal, or multi-agent behavior.
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EvaluationSkill Evaluation with Microsoft Waza
Use Waza to test skill routing and output relevance, then separate implementation evaluations to gate code quality. Mock early, run live trials selectively, and enforce both tiers in CI.
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EvaluationBeyond 1–5 Scores: Binary Judges, Outcome Verification, and pass^k
Multi-point judge scales are subjective, transcript-only scoring misses system state, and a single trial can be a lucky run. Four changes that make agent evaluation trustworthy.
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EvaluationHybrid Agent Evaluation: Custom Judges + Azure Foundry Evaluators
Custom judges test scenario facts while Azure Foundry evaluators score general agent behavior. A REST boundary gives C# both views without adding Python to the application.
Artwork: Wheat Field with Cypresses by Vincent van Gogh. The Metropolitan Museum of Art · Public domain
EvaluationEval-Driven Development: Red-Green-Refactor for AI Agents
Every agent change should be justified by a failing evaluation. A disciplined, TDD-style process for evolving AI agents without regressions.