Context engineering is the practice of designing what information an AI model or agent receives at inference time: selecting, structuring, and ordering the data, instructions, tools, and memory that fill the model’s context window so it produces reliable output. It goes beyond prompt wording to managing the whole context pipeline: which documents to retrieve, how to format them, what state to carry across steps, and what to leave out.
As models move into production and agents chain many steps, context engineering becomes a main lever for reliability. The model is fixed, but what you feed it is not. CUBIG’s Syntitan treats that input as a managed data state: it diagnoses readiness, fixes gaps, and binds each run to a fixed, reproducible state, so the context an agent sees is consistent rather than ad hoc.