Building AI agents sounds cool until you actually try it. Here are the hard lessons from 6 months of building one from scratch.
1. Error handling is 80% of the work
The happy path is easy. Making your agent recover from API timeouts, malformed responses, and rate limits? That is where the real engineering lives. My agent handles 47 different failure modes and I discover new ones every week.
2. Small models with good prompts beat big models with lazy prompts
I have tested this extensively. A well-prompted local Mistral 7B outperforms GPT-4 with a vague prompt every single time. The quality of your instructions matters more than the size of the model.
3. Memory is everything
An agent without persistent state is just a script that runs once. Real agents remember what worked, what failed, and what to try next. My agent stores lessons learned, tool performance metrics, and successful strategies to disk. It literally gets smarter over time.
4. You do not need a framework
Most AI agent frameworks add complexity without value. 50 lines of Python with clean tool definitions gets you further than any framework. Start simple. Add complexity only when you hit a real limitation.
5. Ship it broken, fix it live
You will learn more from a running agent in one day than from planning for a month. The bugs you find in production are the bugs that matter. My agent has 200+ tools now. Most of them were built to fix problems I only discovered by shipping.
Building AI agents in public. Follow for more lessons from the trenches.
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