context-fundamentals

To stop AI agents from forgetting instructions, apply context engineering techniques like token budgeting and progressive disclosure to prioritize critical constraints and prevent context degradation as message history expands.

The lost-in-the-middle problem in agent context windows occurs when models overlook critical instructions buried in long prompts; mitigating it requires placing high-signal constraints at attention-favored positions at the beginning and end.

Structure AI agent context within token limits by applying token-aware budgeting heuristics that prioritize essential system prompts, retrieved documents, and tool outputs while eliminating attention dilution from unnecessary text.

Use progressive disclosure patterns for LLM agents when initial context loads only skill summaries and fetches detailed evidence on demand, reducing wasted tokens and keeping the effective context window focused on high-signal data.

Agents ignore retrieved documents and tool outputs during long sessions due to context degradation from history bloat; resolving this requires debugging effective context capacity and restructuring tool output verbosity.

You can debug system prompt failures and message history bloat without changing agent architecture by applying component-specific heuristics that restructure existing context placement and enforce strict token budgets.