eval-cache

To skip redundant plan evaluations, you can check for a cached result before initiating a new one. This verifies if a valid plan evaluation exists and is up-to-date relative to the original plan, bypassing the expensive evaluation step to optimize workflow execution.

A plan evaluation cache is a mechanism that stores previous evaluation results to avoid redundant computations. You need it in iterative development cycles where plan stability is common, ensuring efficient resource utilization when previous evaluations are still valid.

To check if a cached plan evaluation is valid, run a cache check script to verify if a plan-evaluation.json file exists and is up-to-date relative to plan.json. A fresh cache hit allows you to conditionally skip the plan evaluator agent entirely.

Yes, plan evaluation caching works for multi-step AI workflows. If the plan hasn't changed since the last evaluation, caching ensures the expensive evaluation step is bypassed, effectively speeding up the overall process and optimizing workflow execution.

You should not use a cache check for workflow optimization when your development cycle lacks plan stability. If the plan changes frequently between iterations, previous evaluations will rarely be valid, making the cache check an unnecessary overhead instead of a resource saver.