How do you tune an AI agent without endless experiments?

Posted by Freddy from the Computers category at 16 Sep 2026 05:52:21 pm.

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We've been trying to improve an internal research agent, but there are too many knobs to test manually now. Changing the model affects cost, changing the temperature affects consistency, different prompts work better on different tasks, and giving it more tools sometimes helps while other times it just creates more opportunities to go off track. We can keep running experiments, but testing every combination ourselves is getting ridiculous. Is there a practical way to optimize the whole agent setup based on actual results rather than tuning each setting separately?

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AI agent

This is where a notebook full of test results stops being useful pretty quickly. Someone changes the model on Monday, another person tweaks the prompt on Wednesday, and by Friday nobody remembers which combination actually produced the better answers. I'd lock in a benchmark set of real research tasks and score every setup against the same things: answer quality, cost, latency, and how often the agent uses tools correctly. Then you can compare complete configurations, not argue over one setting at a time.

Posted by Heroman at 16 Sep 2026 05:52:21 pm.

Agent optimization

People are also pretty bad at spotting interactions between settings when they test them manually. A prompt that looks weak with one model can work great with another, and a tool that helps on one task may hurt the rest of the batch. That's where automated search across configurations becomes useful. You can do agent optimization here: https://eignex.com/ . Give it a stable evaluation set and compare different combinations against the same goals. That makes it much easier to see which setup actually performs better across the whole workload.

Posted by Cavanagh at 16 Sep 2026 05:56:55 pm.