The 6 lessons I learned while Building AI Agents.

Hey guys,

If you've ever tried building AI agents that run reliably in real-world scenarios, you know how tricky it can be. Most of us face issues like agents forgetting tasks, misusing tools, or simply hallucinating nonsense.

Here are some of the things that helped me get my agents more reliable and would love to take you input on it:

1. Limit the number of tools per agent: Overloading agents with too many tools increased Nr. errors and unpredictability. Keeping it to around 3 tools seems to strike the right balance.

2. Know when NOT to use an AI agent: Sometimes a deterministic automation is a better fit,simpler, faster, and less prone to errors.

3. Add evaluation steps early: Building testing and labeling workflows upfront to catch errors and optimize cost.

4. Human-in-the-loop integration: Having checkpoints where humans can intervene or approve keeps the system safe and reliable.

5. Log and monitor everything: You can't improve what you can't measure. Detailed logging plus meta-evaluation frameworks helpped me catch subtle bugs.

6. Multi-agent orchestration over mega-agents: Instead of one giant AI trying to do everything, coordinate smaller specialized agents to prevent "context rot" and improve focus.

All these tactics are about creating clean, testable AI systems that don't just look good in demos but work consistently under pressure.

I'm curious , what’s your experience with AI agents in production? Have you noticed any reliability hacks or pitfalls that others should know about?

Thank you.

If you found this useful, you can checkout my full breakdown here: https://www.youtube.com/watch?v=YwlueJNo-qU