I recently developed several AI agents and was surprised by how well they performed. Here are 10 important lessons I learned while building effective AI agents:
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Build your functions first. Create and test all your functions before connecting them to any LLM. Functions are the most reliable part of your system. Make sure they work perfectly before building the actual agents.
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Begin with basic, flexible functions. Something like command line access can handle most tasks. You don’t need to create dozens of specialized functions right away.
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Use one agent initially. After you have your core functions ready, test everything with a single reasoning agent. Most frameworks include built-in reasoning agents. You just connect your functions to it.
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Use top-tier models. Your system will have enough issues without adding model limitations. Begin with GPT-4 or Claude 3.5. You can switch to cheaper options later.
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Monitor everything your agent does. Building agents is like conducting experiments. They will behave unexpectedly. You need detailed monitoring. Tools like Weights & Biases or Phoenix help with this.
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Find what’s slowing you down. Sometimes one agent with basic functions is enough. If not, check your logs to find the problem. Maybe the context is too long, functions aren’t specific enough, or the model lacks certain knowledge.
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Fix problems systematically. You can improve in many ways: use multiple agents, write better instructions, create specialized functions, etc. Pick solutions based on your specific bottleneck.
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Mix structured processes with agents. If your task has clear steps that always happen in order, use a pipeline where each step can be an agent. For example, a research system might have two stages: gathering information broadly, then writing a focused report.
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Pro tip: Use files as memory. Files help agents remember things, take notes, and share information. This saves a lot of context space when agents share file paths instead of full content.
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Pro tip: Learn from existing systems. Study successful agent implementations. Even closed-source systems often share architectural insights that you can apply to your own projects.