I’m struggling with getting my AI agents to work properly. They don’t seem to use their functions reliably and the outputs are often inconsistent. What techniques do you all use for improving agent performance and tweaking their configuration?
I tried using some monitoring tools which helped a bit, but I’m still spending too much time manually checking each run and making adjustments to the prompts. The process feels really tedious.
I’m looking for better approaches to optimize my agent setup, especially when I add new functionality. Any suggestions for workflows or tools that make this easier?
Structured evaluation frameworks saved my sanity with inconsistent agent behavior. Instead of guessing what’s wrong, I built scoring rubrics that track function call accuracy, response relevance, and output consistency. Treat optimization like a data problem, not a guessing game.
I log everything now - not just pass/fail, but reasoning steps, function choices, and decision points. This shows patterns you’d never catch in manual reviews.
For config management, run parallel environments. Before pushing new features, I deploy to a shadow environment and test historical queries on both versions. Catches regressions before they break production.
Don’t sleep on temperature and sampling parameters - they’re huge for consistency. Lower temps usually make function calling more reliable but less creative. You’ve got to experiment systematically to find your sweet spot, not just tweak randomly.
Function reliability was driving me crazy until I discovered batching test cases. Instead of testing agents one by one, I run the same prompt variations across multiple agents at once and compare what comes out.
Building a simple validation pipeline changed everything. I automated checks that flag when outputs drift too far from where they should be. Cuts testing time by hours.
When adding new functionality, I always baseline test before touching anything. Clone your working config, add one function, test against your baseline metrics. You’ll know exactly what broke and when.
Prompt versioning saved my sanity. I keep a basic log of changes and their impact scores. Makes rollbacks way faster when things go sideways.
One more tip - if you’re adding multiple functions, test each one separately first. Learned this the hard way when functions started messing with each other and I couldn’t pinpoint the culprit.
Manual checking destroyed my productivity until I went full automation. You’re doing what I used to do - babysitting every run.
I set up automated feedback loops that test and adjust agent parameters based on performance metrics. Instead of tweaking prompts by hand, I built workflows that A/B test different configurations and auto-pick winners.
The game changer? Automating the entire optimization cycle. My setup monitors agent performance in real time, catches output drift, and triggers retraining or parameter adjustments without me lifting a finger.
For new functionality, I automated staged rollouts. New functions get tested in isolated environments first, then gradually roll out to production agents with automatic rollback if performance tanks.
Those monitoring tools are fine but they just show you problems. You need something that fixes them automatically. I built my entire agent optimization pipeline using automation workflows that handle everything from testing to deployment.
Latenode makes this dead simple - you can create complex automation workflows without coding. I use it to connect all my AI tools and build these self-optimizing agent systems.
