Practical AI guides
Evaluation & debugging
Choose checks that expose regressions, inspect agent behavior, and make failures easier to diagnose.
Where to start
Separate answer-quality evaluation from operational tracing and security scanning. Each answers a different question. Begin with a representative failure you want to detect, then examine coverage and blind spots.
- Which failure does this check detect?
- What does the reported benchmark actually measure?
- How will results change a release decision?
Practical guides
AI Agent Prompt Injection: How to Authorize Actions
AI agent prompt injection: Keep retrieved content from authorizing actions. Bind proposals to the user, task, arguments, target and destination.
Explore the guideHow to Evaluate AI Agents With Multiple Correct Answers
How to evaluate AI agents: Define outcomes, constraints and rubrics that accept valid paths. Build a support-agent test matrix and regression checklist.
Explore the guideWhy AI Responses Are Slow: Trace Retrieval, Tools and Models
Why AI responses are slow: Separate first-answer delay from completion and follow the critical path. Choose a measurement before changing infrastructure.
Explore the guideExplore the reports
Phoenix for LLM Evaluation: Traces to Regression Cases
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