hamel-husain

To diagnose LLM agent production failures, you should reframe evaluation quality as the primary bottleneck instead of guessing at prompt or model iterations. Use real production traces to create a labeled eval set with human-validated rubrics to identify actual failure modes.

Build an eval set from production traces by sampling real failure traces from your deployed LLM agents. Label these traces using human and domain judgment to turn them into an eval set that can reliably measure improvement before you change prompts or architecture.

Set up an LLM-as-judge only after human-labeled rubrics exist for your eval set. You must ensure the judge scoring aligns with and correlates to real human judgments before scaling the automated evaluation of your LLM agents.

Agent prompt changes feel random because they rely on vibes or isolated tweaks rather than a labeled eval set. Treating evals as the source of truth helps identify specific failure modes and directs iteration sequencing for reliable quality improvements.

The best way to decide between investing in eval infrastructure, hiring a team, or productizing expertise is to apply eval-driven decision making. Use your human-validated eval set to measure actual improvement potential and identify whether failures stem from prompt, model, or evaluation bottlenecks.

You should not improve your agent without aligning judge scores to human correlation. Using an LLM judge without human-validated rubrics risks scaling inaccurate evaluations, making prompt and architecture changes ineffective for resolving real production failures.