Law 01 · Context & Reliability
Law of Context Decay
Most agent failures start with the wrong context.
The principle
Most bad outputs come from missing, stale, or conflicting context, not from a model that can't think. The model often reasons fine over the picture it was handed and still lands wrong, because the picture was wrong to begin with. Bad context produces confident bad answers.
Why it happens
A model treats the context window as the world. If that window contains a stale record, a missing constraint, or two facts that conflict, the model has no reliable built-in sense that one is old or suspect. Preference tuning can make this worse by nudging the model toward the framing it was given. The result is not always weak reasoning. Often it is competent reasoning over a bad picture of reality. A stronger model can still fail if the context it sees is stale, partial, or contradictory.
Watch for
- The same question gives different answers depending on which session or document was loaded first.
- Outputs confidently reference facts that are real but out of date, or contradict a source you know is in the window.
- Bumping to a larger or newer model produces no measurable accuracy gain on the failing cases.
In practice
Your support agent keeps insisting a customer's subscription is active when it was cancelled last week, so the team files a ticket to upgrade to a smarter model. The real culprit: the RAG pipeline pulls a 30-day-old cached account snapshot, and the agent reasons flawlessly over stale data. Before swapping models, log the exact context the agent saw on three bad runs; you will usually find a contradiction or a stale record, not a dumb model. Fix the freshness and the 'reasoning bug' evaporates.
Apply it
- On every bad run, dump and read the exact context the model saw before blaming the model.
- Stamp each retrieved fact with its source and timestamp, and drop or refresh anything past a freshness threshold.
- Detect contradictions in the assembled context and surface them instead of silently concatenating both.
The takeaway
Before you reach for a bigger model, look at exactly what the agent saw. Fix freshness, relevance, and contradictions first. A lot of bugs that look like bad reasoning disappear once you do.