Your Agent's Context Window Overflowed and It Answered Anyway
Your agent works great in the demo. Then someone hands it a real ticket with a 40-message thread, three attached logs, and a stack trace, and it confidently answers using the first half of the context — because the second half fell off the back of the window. No error. No exception. Just a quietly wrong answer with full confidence. This is context overflow, and it is one of the most…
Your agent performs well in demos, but when presented with a real ticket containing a 40-message thread, three attached logs, and a stack trace, it confidently answers using only the first half of the context. This is called context overflow and it is a major under-instrumented failure mode in production agents. The model-as-judge will not catch this issue, and it should not be asked to.
Context truncation is an observable, deterministic fact about what actually entered the model, making it a Tier 1 problem that should be treated as such. Most agent frameworks silently truncate, dropping overflow from the middle or oldest turns. When evaluating agents, teams focus on the output text but never inspect the resolved input.
They ask if the answer looks good instead of whether the evidence the agent needed actually entered the model. This is different, and only the latter question can be answered without opinion. To address context overflow, capture the fully-resolved prompt before sending it to the model. Implement a gate to check if the prompt fits within the window and if all required piece of evidence is present.
This gate should be executed deterministically before spending a judge token. By inspecting artifacts the agent didn't create, such as the byte count of the assembled prompt, the presence of a chunk ID, and the embedding of the input, you can identify and prevent Tier 1 problems like context overflow.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — it may contain errors, so check the original before relying on it.
