How to optimize prompts for AI agents?

[Anthem]

The team recently pushed an update to the AI agent and it completely tanked the logic. Every query started producing massive hallucinations, making the bot totally useless for any real work. Had to roll everything back to the previous version just to keep things operational. Now the guys need to properly run the new model through the existing queries and tune the prompts for the specific version before attempting another deployment. What tools are available for this kind of testing and prompt optimization across different model versions?

 

[Tracker]

Switching to a newer model version pretty much guarantees the agent starts behaving differently, since the weight changes are significant enough that prompts which worked flawlessly before suddenly produce garbage. Comparing builds side by side has become the default workflow for anything running in production. Many devs put together local evaluation setups with Promptfoo to get ahead of these shifts. The tool is open-source and handles automated testing, so regressions get flagged internally before any of that mess surfaces for end users.

 

[Chillus]

Catching weird edge cases manually is a nightmare when you're dealing with thousands of real user inputs. Setting up an automated testing pipeline is the only realistic way to maintain stability through major version changes. You can do prompt optimization here https://eignex.com/ . The system runs systematic performance evaluations across different model versions, flags weak spots in the logic and helps dial in the parameters before anything goes live.