The 90% AI Agent Problem
Episode Summary
Building an AI agent that works is easy. Building one that keeps working is where most teams fail.
This episode breaks down the hidden 90% of agent engineering: context management, memory, tool execution, state recovery, and loop closure. I use Claude Code as the reference point, compare it with more fragile agent designs, and show how production quality often comes from code around the model, not from model changes themselves.
What We Cover
- The five pillars of reliable agent architecture
- Why the real divide is LLM capability vs. code orchestration
- Three ways production agents call LLMs
- What “features exist, but loops aren’t closed” means in practice
- How code-only changes moved a production agent from 40% to 60%