Long-running AI coding agents often degrade in performance during extended tasks, a phenomenon caused by context window saturation. As conversation turns accumulate—reaching 80 or more—agents may experience memory errors, re-suggest rejected fixes, or contradict earlier decisions. This occurs because the context window functions like a limited RAM space rather than a hard drive; without managing information, models struggle to process relevant data buried under redundant tool outputs and conversation history.

Effective context management requires compaction, a lossy compression technique that replaces sprawling history with a dense representation of essential information. Unlike mechanical trimming, which drops the oldest messages, or total clearing, which induces amnesia, compaction uses an AI model to selectively retain decisions, state, and constraints while discarding the deliberation process. Failure to execute this properly leads to four primary issues: poisoning, where hallucinations are cemented as fact; distraction from superfluous details; confusion; and cumulative erosion, where repeated paraphrasing degrades task instructions over multiple iterations.

A successful compaction strategy focuses on preserving durable facts and explicit user constraints. Key items to keep include current file states, completed subtasks, and specific next steps, while discarding raw tool outputs, pleasantries, and intermediate deliberations. User-defined constraints, such as 'never touch the auth module,' are critical and should be carried forward as exact text rather than paraphrased. Production tools like Claude Code and OpenAI's Codex CLI utilize different compaction methods, with community consensus suggesting that auto-compaction should trigger at 85–90% capacity to ensure the context remains high-quality enough to summarize effectively.

Developers building their own agents should prune stale tool output before initiating a summary, keep recent conversation turns verbatim to preserve active task flow, and inform users when a compaction event occurs to avoid behavioral confusion. Providing a strict system prompt—mandating that the summarizer avoid creativity and explicitly preserve constraints—remains the most effective defense against context-driven hallucinations. Ultimately, managing a long-running agent requires the deliberate selection of what the system is allowed to forget, ensuring that essential constraints outlive the transient data that fills the context window.

Long-running AI coding agents often degrade in performance during extended tasks, a phenomenon caused by context window saturation. As conversation turns accumulate—reaching 80 or more—agents may experience memory errors, re-suggest rejected fixes, or contradict earlier decisions. This occurs because the context window functions like a limited RAM space rather than a hard drive; without managing information, models struggle to process relevant data buried under redundant tool outputs and conversation history.

Effective context management requires compaction, a lossy compression technique that replaces sprawling history with a dense representation of essential information. Unlike mechanical trimming, which drops the oldest messages, or total clearing, which induces amnesia, compaction uses an AI model to selectively retain decisions, state, and constraints while discarding the deliberation process. Failure to execute this properly leads to four primary issues: poisoning, where hallucinations are cemented as fact; distraction from superfluous details; confusion; and cumulative erosion, where repeated paraphrasing degrades task instructions over multiple iterations.

A successful compaction strategy focuses on preserving durable facts and explicit user constraints. Key items to keep include current file states, completed subtasks, and specific next steps, while discarding raw tool outputs, pleasantries, and intermediate deliberations. User-defined constraints, such as 'never touch the auth module,' are critical and should be carried forward as exact text rather than paraphrased. Production tools like Claude Code and OpenAI's Codex CLI utilize different compaction methods, with community consensus suggesting that auto-compaction should trigger at 85–90% capacity to ensure the context remains high-quality enough to summarize effectively.

Developers building their own agents should prune stale tool output before initiating a summary, keep recent conversation turns verbatim to preserve active task flow, and inform users when a compaction event occurs to avoid behavioral confusion. Providing a strict system prompt—mandating that the summarizer avoid creativity and explicitly preserve constraints—remains the most effective defense against context-driven hallucinations. Ultimately, managing a long-running agent requires the deliberate selection of what the system is allowed to forget, ensuring that essential constraints outlive the transient data that fills the context window.