I've been writing some AI Agents lately with LangGraph and they work much better than I expected. Here are the 10 learnings for writing AI agents that work:

1. Tools first. Design, write and test the tools before connecting to LLMs. Tools are the most deterministic part of your code. Make sure they work 100% before writing actual agents.

2. Start with general, low-level tools. For example, bash is a powerful tool that can cover most needs. You don't need to start with a full suite of 100 tools.

3. Start with a single agent. Once you have all the basic tools, test them with a single react agent. It's extremely easy to write a react agent once you have the tools. LangGraph a built-in react agent. You just need to plugin your tools.

4. Start with the best models. There will be a lot of problems with your system, so you don't want the model's ability to be one of them. Start with Claude Sonnet or Gemini Pro. You can downgrade later for cost purposes.

5. Trace and log your agent. Writing agents is like doing animal experiments. There will be many unexpected behaviors. You need to monitor it as carefully as possible. LangGraph has built in support for LangSmith, I really love it.

6. Identify the bottlenecks. There's a chance that a single agent with general tools already works. But if not, you should read your logs and identify the bottleneck. It could be: context length is too long, tools are not specialized enough, the model doesn't know how to do something, etc.

7. Iterate based on the bottleneck. There are many ways to improve: switch to multi-agents, write better prompts, write more specialized tools, etc. Choose them based on your bottleneck.

8. You can combine workflows with agents and it may work better. If your objective is specialized and there's a unidirectional order in that process, a workflow is better, and each workflow node can be an agent. For example, a deep research agent can be a two-node workflow: first a divergent broad search, then a convergent report writing, with each node being an agentic system by itself.

9. Trick: Utilize the filesystem as a hack. Files are a great way for AI Agents to document, memorize, and communicate. You can save a lot of context length when they simply pass around file URLs instead of full documents.

10. Another Trick: Ask Claude Code how to write agents. Claude Code is the best agent we have out there. Even though it's not open-sourced, CC knows its prompt, architecture, and tools. You can ask its advice for your system.