Most agentic AI systems don't fail because the LLM is bad.

They fail because of how the system around it was built.

We Partnered with Paul Iusztin (Decoding AI) that covers exactly this. He breaks down 6 engineering mistakes that kill agents in production, often quietly.

The 6:

1. Context window mismanagement: treating it as a dump, not working memory

2. Overengineered architecture before the problem actually requires it

3. Using agents where a deterministic workflow does the job

4. Brittle output parsing that breaks under real data

5. No planning logic in the tool loop, just reaction

6. No eval framework from day one, so degradation stays invisible

These don't fail you individually. They compound.

Full guide: decodingai.com/p/agenti…