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You can optimize AI agent performance by running execution traces through a diagnostic loop that identifies where outputs deviate from requirements, applies counterfactual reasoning to prevent regression, and performs targeted prompt mutations.

Trace-informed diagnosis for prompt tuning is a process that analyzes full execution traces to pinpoint exact deviations from scoring criteria, enabling targeted system-prompt refinement instead of blind hill-climbing.

You prevent regression during iterative prompt engineering by applying counterfactual reasoning to evaluate why previous attempts failed, combined with an additive-only safety valve that switches to conservative mode after repeated regressions.

To run a multi-candidate evaluation loop for system prompts, you need execution traces, scoring criteria, a filesystem-based candidate management system, and a set of test prompts for the evaluation loop.

AI agent performance plateaus during optimization because standard hill-climbing approaches lack root-cause analysis, whereas a trace-informed diagnostic loop identifies the exact failure points and applies targeted mutations to break through the plateau.

You should not use counterfactual diagnosis for skill tuning when you lack execution traces or scoring criteria, as this method requires both to perform root-cause analysis and prevent regression during the optimization loop.