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Most AI agents are great engineers, but poor scientists.

They find a solution that works okay and spend their entire compute budget making tiny improvements instead of trying a radically different approach.

This is the local optimum trap.

When an agent narrows in on one path too early, it stops discovering and starts polishing.

I condensed the mechanics of how to measure this into a visual field guide — swipe through below.

The FML-bench framework distinguishes between two types of agent behavior:

The Tuner changes a learning rate by 0.01 to get a marginal gain.

The Explorer asks if the entire loss function is wrong and tries a structural change.

For an ops leader, this is the difference between an automation that slightly optimizes a report and one that discovers a more efficient way to handle the entire data intake process.

Turning a manual guess-and-check workflow into a breadth-first automated exploration prevents teams from settling for the first mediocre solution that passes a test.

The 12-page guide breaks down the evaluation pipeline and includes diagrams of narrow versus broad search patterns.

How do you currently verify if your AI agents are actually exploring new solutions or just iterating on the first prompt that worked?

#YourBrand #LLMOps #WorkflowAutomation #BusinessAnalysis #FMLBench