The agent that forgets the goal
On a long task, as the context window fills and older turns get truncated, the agent can drift from or forget its original goal, which looks like the model quietly getting worse for no reason.
Twenty turns into a research task, the agent has lost the original question and is confidently answering a subtly different one.
where_are_you
The maturity ladder, worst to best
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It can drift or forget the goal; older content just gets truncated
We cap turns to avoid overflow, nothing smarter
We summarize/compress history to keep the goal in view
Structured memory, and the invariant goal is re-anchored each turntarget
why_it_matters
Context is finite, and a long agent run fills it. When it overflows, the oldest content, which often includes the original instruction, gets truncated first. The agent doesn't announce this. It just starts optimizing for whatever's still in the window, which may be a sub-goal, a tangent, or the shape of the last few tool results. From the outside it reads as the model getting dumber partway through a task, when really it has lost the thread.
Keep the goal in view as context fills. Summarize or compress history so the window holds meaning rather than raw transcript, and re-anchor the invariant goal each turn so it can't be truncated away. Structured memory, where the durable facts live outside the rolling context, is stronger still. Context rot is a slow failure, so it survives casual testing and shows up only on the long tasks that matter most.
more_failure_modes
Related ways agents break
See all 7 failure modes, or score your loop with the scorecard.
faq
Questions & answers
- Why does my agent lose track of the task on long runs?
- Because the context window filled and the original goal got truncated with the oldest turns. The agent then optimizes for whatever's left in the window, which drifts from the real task. Summarize or compress history to preserve meaning, and re-anchor the invariant goal on every turn so it can't be dropped.
- What is context rot in an AI agent?
- It's the slow degradation that happens as a long run fills the context window and important early content, including the goal, gets truncated. The model appears to get worse partway through the task. The fix is managing what stays in context: compression, structured memory, and re-anchoring the goal rather than letting raw transcript push it out.
Fixing one failure mode is a day. Hardening the whole loop is the work.
I review where your agent loop terminates, how it escalates, how it fences tool output, idempotency on the side effects, and the evals that catch a regression before users do. Book a call, or leave your email.
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