After a release, overall task success drops. You suspect either deg…

Question

After a release, overall task success drops. You suspect either degraded memory retrieval, a broken tool contract, or a prompt regression—but a single end-to-end pass/fail metric cannot tell you which. What evaluation approach should you implement?

Accepted Answer

Design layered evaluations that separately score memory/recall fidelity, tool-call correctness and schema validity, prompt adherence, and end-to-end task success so regressions localize to a subsystem — Choose A. When multi-agent quality slips, you need component-level evals: memory and knowledge retrieval quality, tool selection/arguments/validation, prompt adherence, plus E2E success. That localization tells you whether to fix vector indexes, MCP tool schemas, or prompt versions. Latency-only misses correctness. Rubric-free binary gut feel is noisy. Skipping subsystem evals is how teams thrash on the wrong layer. The blueprint explicitly calls out evaluations for memory, knowledge, tools, and prompts.

Answer

Only measure average latency and ignore functional quality

Answer

Collapse all signals into one binary “looks good” label from an intern with no rubric

Answer

Stop evaluating memory and tools because end-to-end success alone is always sufficient

After a release, overall task success drops. You suspect either degraded memory retrieval, a broken tool contract, or a prompt regression—but a single end-to-end pass/fail metric cannot tell you which. What evaluation approach should you implement?

Short Explanation

Full Explanation