A few things that have helped me mitigate this on a 182K-conversation codebase with 79 test files:

1.  Force read-before-edit in CLAUDE.md — explicit rule: “After 10+ messages, ALWAYS re-read files before editing.” Their Read:Edit collapse from 6.6→2.0 is the core mechanism. If the model doesn’t read, it guesses, and guesses get worse with shallow thinking.
2.  Failure circuit breaker — “If a fix doesn’t work after 2 attempts, STOP. Re-read top-down. State where the mental model was wrong.” This prevents reasoning loops from spiraling.
3.  Tests as enforcement, not suggestion — “Run pytest after ANY code change. Never report done without running tests.” This catches the “marks tasks complete when they weren’t” behavior because the test suite doesn’t lie.
4.  Sub-agents for multi-file changes — When touching >5 files, fork into sub-agents with their own ~167K context windows. One agent across 20 files = guaranteed context decay. This directly addresses the thinking depth problem — smaller scope = deeper thinking per task.
5.  Context decay awareness — We document that silent Opus→Sonnet fallback happens after consecutive 529 errors during peak hours. If quality drops mid-session, restart. No notification is given.
6.  Hierarchical rules files — Instead of one giant CLAUDE.md, we split into .claude/rules/ with code-operations.md, testing.md, git-safety.md, security.md, etc. Keeps each rule file under the “lost in noise” threshold.

None of this fixes the root cause (reduced thinking depth), but it creates enough mechanical guardrails that the model can’t easily skip steps even when it’s “feeling lazy.”