Preparing the right context is essential when deploying AI agents in the enterprise. Of the 4 Cs, Context serves as the starting point for building out three layers: metadata, semantics and ontology. Through a practical manufacturing example, the article explains how to distinguish between context and precise definitions, and the role of a new type of ontology.
This series organizes the deployment of AI agents around the 4 Cs: Context, Choice, Control and Cost. This installment focuses on context and points out that a lack of context in the field can create a gap between PoC results and production use.
Context refers to all the information an AI receives when determining a response or action, including user instructions, data, conversation history and tool descriptions. The three layers for structuring context are metadata, which describes the structure and provenance of data; semantics, which defines the calculation logic and terminology for metrics; and ontology, which explicitly defines the classification and relationships of the entities covered.
Metadata is divided into three areas: business, technical and operational. If it is missing, AI may perform an incorrect analysis. Semantics functions as an intermediate layer that provides common definitions so that metrics with the same meaning are calculated consistently.
Ontology has traditionally required specialized knowledge and substantial cost, but in the era of generative AI its role is shifting toward delivering context quickly. The article proposes using it selectively by prioritizing formal definitions while complementing them with concepts and relationships extracted by AI, and by providing only the knowledge needed for each question. A practical example is presented in which differences in the definitions of first-pass yield and newly launched part numbers affect decision-making.