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Final performance tells us whether an agent succeeded, but not how it searched.

A lucky guess looks exactly like a robust strategy in a standard pass/fail benchmark.

I spent time analyzing the FML-bench framework to understand why some agents fail complex research tasks despite using the most powerful models.

The problem is the confusion between Model Capability and Search Policy.

Model Capability is the LLM's ability to execute a single step.

Search Policy is the logic that governs what the agent tries next.

Most agents rely on Greedy Hill-Climbing, where they always take the immediate best-looking step.

This leads them straight into local optima, which are essentially dead ends that look like progress.

The most reliable agents prioritize Exploration Diversity.

Instead of making slight variations to one idea, they test fundamentally different approaches to the problem.

For a business analyst, this means the biggest lever for reliability is no longer the prompt, but the search topology.

Moving from prompt engineering to strategy engineering turns a fragile automation into a systematic exploration process.

I condensed the framework and the search topology diagrams into a 12-page field guide below.

How do you currently track whether your agents are actually following a logical path or just stumbling upon the right answer?

#LearnWithVenkat999 #LLMOps #WorkflowAutomation #BusinessAnalysis #AIAutomation