How to build a context layer for your AI SDLC
Blog post from Port
A context layer serves as an essential component in the AI software development lifecycle (SDLC) by providing a unified, continuously updated model of an organization's engineering estate, which AI agents and people can query efficiently. This layer houses vital information such as services, their ownership, dependencies, and the actions agents can take, streamlining the process and reducing costs by eliminating the need for multiple API calls. Implementing a context layer can significantly reduce per-query agent costs by up to 80%, as measured by Port. It includes four types of context: domain knowledge, operational state, human layer, and actions, all wrapped with governance controls. This structure ensures that agents can operate deterministically and safely, preventing errors and inefficiencies caused by disparate data sources and inconsistent information. While context layers are distinct from semantic layers, which focus on consistent data definitions for analytics, they complement each other by providing operational grounding and governed actions. Building or buying a context layer involves acquiring the foundational elements like relation graphs and governance controls, while the specific model is customized to the organization's unique data and operational decisions, enhancing agent efficiency and effectiveness across the SDLC.
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