Why Your AI Agent Failed to Remember: Context Window Management in Claude

You built a shiny new AI agent. It runs for a few turns, answers questions beautifully — and then suddenly hits a wall. The agent stops understanding earlier context, gives nonsensical answers, or costs you a fortune in token spend. What happened? Context window management. Most developers overlook this until it breaks. But if you're building production AI agents, managing context is non-negotiable. I've shipped agents that died silently from poor context handling, and learned the hard way. Let me save you that pain. The Problem: Context Isn't Free Claude's context window is large (100K–200K tokens depending on the model), but it's not infinite. Every message, system prompt, and tool call eats into it. When you hit the limit: You lose older messages — the agent forgets conversation history Token costs spike — each new message reprocesses remaining context Quality degrades — without history, decisions get worse Users get frustrated — "But we literally just talked about this!" Most agent