Some things we've[0] learned on agent design:

1. If your agent needs to write a lot of code, it's really hard to beat Claude Code (cc) / Agent SDK. We've tried many approaches and frameworks over the past 2 years (e.g. PydanticAI), but using cc is the first that has felt magic.

2. Vendor lock-in is a risk, but the bigger risk is having an agent that is less capable then what a user gets out of chatgpt because you're hand rolling every aspect of your agent.

3. cc is incredibly self aware. When you ask cc how to do something in cc, it instantly nails it. If you ask cc how to do something in framework xyz, it will take much more effort.

4. Give your agent a computer to use. We use e2b.dev, but Modal is great too. When the agent has a computer, it makes many complex features feel simple.

0 - For context, Definite (https://www.definite.app/) is a data platform with agents to operate it. It's like Heroku for data with a staff of AI data engineers and analysts.

Be careful about what you hand off to Claude versus another agent. Claude is a vibe project monster, but it will fail at hard things, come up with fake solutions, and then lie to you about them. To the point that it'll add random sleeps and do pointless work to cover up the fact that it's reward hacking. It's also very messy.

For brownfield work, work on hard stuff or work in big complex codebases you'll save yourself a lot of pain if you use Codex instead of CC.

It's quite worrying that I have several times in the last few months had to really drive home why people should probably not be building bespoke agentic systems just to essentially act as a half baked version of an agentic coding tool when they could just go use Claude code and instead focus their efforts on creating value rather than instant technical debt.