How to design context for your AI software factory

Context design is presented as a core discipline for AI software factories, determining what organizational knowledge agents receive, what data remains live, how information is refreshed, and how decisions are recorded and governed. The piece argues that agents working across software-development tools need a structured context layer that connects services, repositories, teams, on-call schedules, deployments, runbooks, incidents, and policies, rather than repeatedly reconstructing these relationships through MCP tool calls. It divides this layer into semantic context for organizational entities and their relationships, operational context for current conditions such as deployments and ownership, and decision context for prior incidents, approvals, and agent actions. Citing tests of 12,000 production SDLC queries, it reports that agents using pre-related context were about 80% cheaper than agents navigating multiple MCP servers, while also asserting that excessive context can reduce accuracy. Examples including autonomous ticket resolution, self-healing incident response, and AgentOps illustrate differing needs for routing, freshness, provenance, access control, and auditing. The article recommends that platform engineering teams own context modeling as code, define schemas and freshness targets, limit each agent’s visibility, and continuously improve the model from observed workflows, while using MCP for information that is cheaper or more appropriate to retrieve live.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.