Most AI Agent Failures Aren't Model Problems, They're Architecture Problems
Most AI agents that fail in production were built by smart teams using capable models. The failure was not in the intelligence of the LLM. It was in the architecture surrounding it. Specifically, three things: an unmanaged context window, a monolithic instruction set, and a missing governance layer. Fix those three things and your agent goes from demo-ready to production-grade. This article shows you exactly how.
The Agent Architecture Most Teams Get Wrong聽聽聽
An AI agent is an LLM running in a loop, calling tools, reasoning about what to do next, and iterating until it produces a final response. Five components make up this loop:
- A system prompt defining behaviour and constraints聽聽聽
- A user prompt providing the task or question
- Tools such as APIs, search, databases, and code execution
- An agent reasoning step where the model plans decides
- A final output returned to the user
This architecture...
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