Engineering teams have always struggled with fragmented documentation, tribal knowledge, and inconsistent standards. What we now call context engineering is closely related to long-standing knowledge management challenges, but the impact is amplified as AI agents enter everyday development workflows. Gaps, conflicts, and poorly organized context slow developers and lead automated systems to produce unreliable results. Without deliberate strategies, organizations risk scaling confusion instead of capability.

In this session, we will explore practical ways to engineer context and knowledge management that works for both developers and AI agents. Using real examples from building centralized, docs-as-code platforms, we will cover how to organize internal documentation, define ownership between team and platform context, and validate content for gaps and conflicts. We will also examine shared context engineering strategies such as summarization, retrieval, and externalization, including how MCP-based approaches improve clarity, scalability, and execution for humans and machines alike.