Codex context auto-compaction loop rereads files, loses progress, and burns paid credits
edisonjoao1 (OpenAI Codex user) · Jul 24, 2026 · Coding agent
What happened
A long-running Codex project chat on GPT-5.6 Sol entered a context auto-compaction loop. After each 'Context automatically compacted' event, Codex retained the broad project objective but appeared to lose execution progress. It repeatedly reread the same files, reloaded the same skills or memory files, restated that it had found the exact defect, promised the next update would contain the completed change, then compacted again before completing the edit. The requested implementation was never completed, and the loop consumed roughly 10%-15% of the user's available paid Codex usage. Context was 93% full at 240,000 / 258,000 tokens when captured.
Causal vector
Context auto-compaction loop: after each compaction event the agent reread the same files, restated the same plan, promised completion, then compacted again without editing
Source
Reported by GitHub issue, openai/codex #35226. Verified against the primary report.
A policy layer that detects repeated file reads and no working-tree progress — and cuts spend when a session is burning credits without advancing — would have stopped this loop before it consumed a tenth of the paid allowance.
The six rule types that contain this class of failure
Per-transaction cap
Any single spend above your ceiling is BLOCKED before it moves.
Daily total
Cumulative spend across all agent calls, bounded per day.
Velocity limit
Stops runaway retry loops — the #1 cause of overnight losses.
Merchant allowlist
Only approved destinations can ever receive funds.
Category rules
Flag high-risk classes (crypto, infra, refunds) for review.
Approval threshold
Above a value, the action waits for a human.
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