Architecture

You’ve built the perfect agentic workflow. MCP servers are connected, functions are documented, and the first few Claude calls are flawless. Then call number seven happens: results drift, hallucinations emerge, and you find yourself debugging why the AI suddenly “forgot” critical instructions you passed hours ago. This is context rot—the degradation of model reasoning quality as context windows fill, compress, and reset in multi-agent systems. It’s architectural, not accidental. And it scales predictably. ...

TL;DR Learn the core design patterns that actually matter for AI systems. See how each pattern maps to real AI orchestration problems. Use concrete code-style examples to plug into your stack. Understand trade-offs, not just theory. Apply a simple roadmap to introduce patterns without over-engineering. The Stateless Problem: Why AI Needs Architecture LLMs are stateless. Every time you send a prompt to GPT, Claude, or any model, it forgets everything outside the current request. It does not remember your previous message, the plan it created two steps ago, or the tools it just called. Any “memory” comes from the system around it, not from the model itself. ...