Context Anxiety
Anti-pattern: a context-aware model misjudges its remaining token budget and wraps up early — summarising, declaring tasks done, cutting corners — while ample context remains, so the harness must manage perceived budget, not real usage.
Problem
The model's belief about its remaining budget is miscalibrated, and that belief, not the actual utilisation, drives its behaviour. Believing it is near the end, it wraps up prematurely: it summarises work that was not finished, marks tasks complete that are not, and takes shortcuts to 'save space'. This is distinct from real degradation when the window genuinely fills — here there is plenty of room, and the failure is a false perception triggering early termination. Because the trigger is the model's reading of its own budget, fixes that only enlarge the real window do not help; the model still panics at the same perceived threshold.
Solution
Decouple the budget the model perceives from the budget it is allowed to use. A reported example enables a one-million-token window but caps real usage at two hundred thousand, so the model never approaches a threshold it is anxious about. Pair this with reminders, repeated rather than stated once, that the task is not near its end and that wrapping up is not yet warranted. Mitigation pattern: structured-note-taking / external memory so progress does not depend on the model's sense of remaining room. Treat any early 'I'll summarise to save space' move as a calibration alarm, not a sign the window is actually full.
When to use
- Reviewing a long-running agent that wraps up or summarises while most of its context window is still free.
- Diagnosing premature task completion on budget-aware models.
- As a harness-design checklist item: does the agent panic about a budget it has not reached?
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