What Happens When Your AI Agent’s Context Window Runs Out Mid-Task

Every AI agent session has a context window, a hard ceiling on how much conversation, code, and tool output it can hold at once. On a long task, that ceiling eventually gets hit, and something has to give. Most tools handle this with compaction: summarizing the earlier part of the session so the agent can keep going without literally forgetting everything that came before. Understanding what that summarization actually preserves, and what it doesn’t, changes how you should structure long-running agent work.

What Compaction Actually Does

When a session approaches its context limit, the tool typically condenses the earlier portion of the conversation into a compressed summary: what was asked, what was decided, what’s been done so far. That summary replaces the full transcript, freeing up space to keep working. The agent doesn’t literally forget the task, but it now knows about the earlier work secondhand, through a summary, rather than firsthand, through the original detail.

What Gets Lost

• Exact wording and edge cases. A summary captures the gist of a decision, not the specific caveat you mentioned once in passing three messages before the summary point. If that caveat mattered, it needs to be restated or written somewhere durable, not left to survive compaction by chance.
• The reasoning trail, not just the conclusion. A summary says what was decided. It rarely preserves why three other approaches were rejected first, which means if the agent’s later work implicitly relies on “we already ruled that out,” it may not actually know that anymore.
• Precision on file paths and line numbers. Summaries tend to compress “I read file X and found the bug at line 40” into “found and understood the bug in file X,” which is a meaningfully different level of detail if a later step needs to reference that exact location again.

Structuring Long Tasks So This Doesn’t Bite You

• Write durable state to a file, not just the conversation. A running task list, a decisions log, or a plan document that gets updated as you go survives compaction intact, because it’s not part of the context that gets summarized, it’s an artifact the agent can re-read.
• Make critical constraints explicit and repeat them. If something is a hard requirement (a security constraint, a rule that must never be violated), it should live somewhere the agent re-reads on each step, not rely on being remembered from early in a long session.
• Break very long tasks into checkpoints. A task designed to complete and verify in discrete stages, each with a clear “done” state, is far more resilient to a mid-task compaction than one long undifferentiated push where “progress” only exists in the conversation history.
• Ask the agent to summarize its own understanding periodically, especially before a natural pause point. Catching a drifted assumption before compaction is far cheaper than discovering it after.

Why This Matters More as Agent Tasks Get Longer

The failure mode isn’t dramatic, the agent doesn’t announce that it’s lost detail. It just quietly works from a slightly thinner understanding than it had before, and the gap only becomes visible when a later step contradicts an earlier decision nobody restated. As agentic workflows take on longer, multi-hour tasks, designing for graceful compaction isn’t an edge case anymore, it’s a basic reliability requirement, the same way you’d design any long-running process to checkpoint its state rather than trust an in-memory value to survive indefinitely.