The Architecture Mistakes That Kill AI Agents in Production

Most AI agent tutorials show you a LangChain loop that "plans, acts, and reflects." Then you try to run it in production, and it burns $47 on a single task, hallucinates a database migration, and your CTO asks you to "just use a chatbot instead."

I've been building and operating AI agents at production scale. Here are the architecture mistakes I see repeated — and the patterns that actually work.

The Maturity Model

Here's how I think about agent architecture maturity:

1. Foundational: Reliable tool use, tracing, regression suites on golden tasks
2. Intermediate: Workflow graphs with retries, compensations, measurable SLIs
3. Advanced: Multi-agent decomposition with shared observability, conflict resolution, cost governance
4. Principal: Org-wide agent platforms — policy engines, audit trails, lifecycle management

Most teams are stuck at step 1, trying to do step 3. Build the foundation first.