Hi folks! I’ve been working on some AI agents lately and I’m struggling with figuring out if they’re actually performing well. Manual testing is getting pretty tedious and I feel like I’m just guessing most of the time. I know there are some tools out there like evaluation platforms but I’m not sure which direction to go. Has anyone here found good ways to test their agents systematically instead of just checking if the responses “feel right”? Would love to hear about any frameworks or approaches you’ve tried that actually give you solid data on performance.
I started tracking agent performance after launching three bots that worked great in dev but fell apart in production. Now I focus way more on conversation flow metrics instead of just checking individual response quality. I track completion rates, how many clarification requests users need, and where people drop off mid-conversation. What’s really helped is creating fake user personas with different communication styles and running them through common scenarios every month. This catches weird edge cases that standard test datasets totally miss. I also monitor response times since I found out users bail if agents take too long processing complex requests. Super simple metric but huge for user experience. Flow analysis plus persona testing gives me way better insight into real performance than accuracy scores ever did.
When I started developing more advanced AI agents, I faced similar challenges. The key for me was implementing robust logging and metrics tracking from the beginning. I created automated test suites that run predefined scenarios against my agents, allowing me to compare the responses to expected outcomes. A significant step forward was the establishment of ‘golden datasets’—carefully curated examples that clearly define successful outputs. Monitoring metrics like response accuracy, completion rates, and response times is crucial. Interestingly, I’ve found that the evaluation criteria vary widely depending on the type of agent, so customer service bots require different metrics compared to data analysis tools. Ultimately, I recommend combining quantitative metrics with qualitative feedback through human assessments for a more comprehensive evaluation of your agents’ performance.
i do a/b testing with real users and run weekly benchmark tasks. key is keeping test cases consistent to see changes over time. also, track different kinds of failures - big difference between the agent completely missing the point or just giving a weak answer.
Learned this the hard way - you need both automated scoring and human review loops.
For automation, I track completion rates and run semantic similarity checks against reference answers. Game changer was setting up eval pipelines that trigger after every model update.
Human review’s equally crucial. I rotate different team members through weekly agent reviews - same person judging quality creates blind spots.
Failure taxonomies helped tons. Instead of marking things “wrong,” I categorize failures: “factually incorrect,” “missed intent,” “format issues,” etc. Makes pattern spotting and targeted fixes way easier.
Also added thumbs up/down after user interactions. Basic but catches issues pure accuracy metrics miss.