Search agents powered by large language models (LLMs) are transforming how enterprises retrieve information.

Rather than requiring users to craft the perfect query, a search agent autonomously decides what to search for, which retrieval strategy to use, and when to stop searching.

It does this across multiple rounds of interaction, refining its approach based on what it has already retrieved.

However, getting this multi-step behavior to work well is hard.

No base model arrives knowing your tools or your environment.

Prompt a small model and you rarely get dependable multi-turn behavior.

Prompt a frontier model and it often works, but you pay for that capability in latency and cost.

Fine-tuning offers a third path: you teach a small model your tools and environment directly.

The result is a small model’s speed and cost with the reliability that would otherwise require a frontier model.

Even though fine-tuning is the natural next step, the traditional approaches each fall short.

Supervised fine-tuning (SFT) depends on expert demonstrations of ideal multi-turn trajectories, which are costly to collect and usually don’t exist for your setup.

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