How context windows actually decay

I kept a single chat thread open for three days while iterating on a refactor. At first the model followed the constraints I fed it: Node 14, CommonJS, knex queries. By day two it started suggesting async functions and syntax that only exists in Node 16 and later. Nothing in the last few messages said "switch runtimes," but recent turns dominated. The model still had the early constraints in the full context, but attention is not uniform. Recent tokens pull harder. You end up with answers that fit the last few edits while quietly violating earlier guardrails.

Hidden assumptions become project defaults

In one bug I chased for half a day, the model kept producing SQL that assumed a case-sensitive column name. Our database used lowercase identifiers and everything failed. The suggestion looked reasonable so I tried it locally. Tests passed against a mocked DB, then CI broke in staging where the real Postgres setup used a different collation. The model had made an assumption about the environment that never got surfaced in prose. That small mismatch propagated into schema migrations and then into a deployment rollback.

Another time the assistant suggested an auth pattern that mirrors OAuth but with an implicit redirect URI. I assumed the snippet was complete and wired it into the integration test; the external service rejected it. The model didn’t mean to lie. It filled gaps with the most probable pattern it had seen during training. Those gaps become defaults unless you force explicit checks.

Tool failures look like reasoning errors

We hooked the model to CI logs and a code search tool. That sounded good on paper. In practice the search API returned truncated JSON during high load and the model improvised the missing fields as if they were real. The reply looked like a chain of reasoning: here is the failing test, here is the fix. But the root cause was a broken tool response, not a failure of logic. The model will happily continue when a tool times out or returns malformed data.

After that incident I started treating every tool call as untrusted. I added simple schema checks and explicit error paths. When the linter API returns partial output we log it and pause the agent instead of letting the model guess the rest. It’s an ugly change: more plumbing, more logs. But it stops a few cascading mistakes that used to show up in PRs.

Practical guardrails I actually use

I added an immutable header at the start of every conversation we keep alive. It is a plain text block with runtime, package manager, test command, and a short checksum of the repo state. Before applying a change I ask the model to echo that header back and fail if it doesn’t match. It sounds manual, but having that explicit anchor reduces drift more than reasserting constraints in conversational replies.

I also shortened sessions. Long threads are convenient but brittle. For exploratory threads we use a shared chat workspace and compare models side by side so drift patterns are visible rather than hidden. When we need confident, source-checked answers we push text and code snippets into a structured research flow so we can validate claims against primary sources and changelogs rather than a single chat reply. That workflow uses a different toolset and a different checklist.

When tiny errors compound fast

One missing semicolon suggested in a refactor was the first domino. It caused a build failure that masked a migration script error. While trying to fix the broken build the model suggested a rename that assumed a column existed. That rename ran on the wrong table and triggered a permissions rollback. The chain felt nonlinear but it was just a string of small unchecked assumptions. Now every model-suggested change goes through three quick gates: unit tests, schema validation of tool outputs, and an interaction log review. I still get surprised, but the surprises are now noisy enough to catch early instead of silently drifting into production.