Context window

Every model has a fixed context window: a limit on how many tokens of prompt, conversation history and generated output fit into a single call. Once a conversation or an agent's working history exceeds that limit, something has to give, older content gets dropped, truncated, or the call fails outright, depending on how the system is built.

An agent that runs many tool calls, reads several documents and reasons across many steps fills its context window far faster than a single chat message would. If a system quietly drops the part of the context holding a policy constraint or an earlier instruction, that is not just a quality problem in a regulated workflow, it is a control that silently stopped applying.

An agent running several web searches and reading multiple retrieved documents in one task can approach a smaller model's window well before the task is actually finished.

In Dynamiq, an agent can enable context summarization: once token usage crosses a configurable share of the model's window, older history is compressed into a summary while the most recent messages are kept verbatim, and the original request is always pinned so a long run never loses track of the task it started with.