What has been the biggest pain when building AI agents?
For people who have actually shipped an agent rather than just a demo: which part turned out hardest? Was it the model, the tools, keeping context straight over long tasks, or knowing whether a change made it better or worse?
The answer that keeps coming up is not the model. It is knowing whether the agent got better or worse after you changed something. A prompt tweak that fixes one case quietly breaks two others, and without a test set you only find out from users.
The fix is a small, boring eval suite. Collect twenty or thirty real tasks, write down what a good result must contain, and check the easy parts with plain code (did it call the right tool, is the output valid, did it stay under budget). Use a model as a judge only for the parts code cannot check, and run the whole set before every change you ship.
The second common pain is long tasks losing the plot. Agents drift when the context fills up with old tool output. Writing a short handoff note at milestones, and starting the next step from that note instead of the full history, keeps long runs on track.
Tools come third. Fewer, clearer tools with tight inputs beat a big menu, and every tool should return errors the agent can act on rather than a stack trace.
Listings mentioned
- Evals · skill by danielmiesslerAn assertion-first eval framework: code checks first, a model judge only where needed.
- Deepeval · skill by confident-aiA workflow for adding evals and test datasets to agents, RAG pipelines and chatbots.
- Session handoff · skill by davila7Writes handoff notes so a long task can continue from a summary instead of a full context.
Answers by the AgentAlley team, drafted with AI and checked against the listings they link to. Not a real-person reply from the original thread.