AWS has published a guide to using Strands Evals for detecting AI-agent failures and tracing their root causes. The workflow produces categorized failures, confidence scores, causal chains, and recommendations for whether fixes belong in prompts or tool definitions.
That kind of diagnosis is becoming necessary as agents move into multi-step production workflows. A failed agent run can involve planning errors, tool misuse, bad context, or brittle instructions, and simple pass-fail tests often do not explain enough.
The post shows how evaluation tooling is expanding from scoring outputs to debugging the systems around agents.