AI Models & Assistants / Knowledge library
Agent Context Management
Selects, compacts, orders, and supplies information for each model turn
Why learn Agent Context Management?
It keeps long-running agents grounded in relevant evidence while respecting context limits and cost.
The problem it solves
It prevents an expanding run history from overwhelming or confusing later model turns.
Where it came from
Context management decides which instructions, conversation items, retrieved evidence, tool results, summaries, and state reach the model. More context is not always better because irrelevant history consumes budget and can distract decisions.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Define Agent Context Management and identify its place in an agent architecture.
- Choose a task where Agent Context Management improves an agent system.
- Diagnose one failure caused by misusing Agent Context Management.
- Interpret a trace or state transition involving Agent Context Management.
- Explain one operational tradeoff of Agent Context Management.
Documentation and sources
- OpenAI Agents SDK: Context management (openai.github.io)