Evidence-first authority page
Context Window Management for AI Agents
Practical strategies for keeping a long-running agent conversation within context limits without losing important information.
Direct answer
Long-running AI agent sessions manage context window limits through summarization of older turns, selective retrieval of only relevant prior context rather than the full history, and explicit state tracking (a running task list or memory file) that persists key facts outside the conversation window itself — relying on the raw conversation history alone doesn't scale past a certain session length.
Last reviewed 2026-08-29
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Three complementary strategies
- Summarization — periodically compress older turns into a shorter summary, preserving key decisions and facts while discarding verbose intermediate steps.
- Selective retrieval — rather than replaying full history, retrieve only the specific prior context relevant to the current step.
- External state — persist important facts (decisions made, task progress) outside the conversation window in a structured format the agent can reference, so they survive even if older turns are summarized away.
Where this matters most
Multi-step agentic workflows that run for many turns — coding sessions, long research tasks, extended customer support threads — are where naive full-history approaches hit limits fastest and where these strategies pay off most.