Giving your team the latest coding agents is only part of the job. You also need to build the infrastructure that lets those agents navigate your company. An agent may be good at reading code and implementing a change, but working inside an organization requires more. Which repository owns this behavior? Where is the relevant design decision documented? How do you investigate a failed deployment? Which tools and workflows does this team use? Engineers learn these things through experience, documentation, and conversations with colleagues. Much of that organizational context is scattered across systems or never written down. Without a reliable way to access it, agents depend on engineers to reconstruct it for every task. Making that context accessible takes more than connecting a document search tool or exposing a catalog of APIs. Agents need to discover the right resources, inspect how a tool works, and follow established workflows without loading the entire company into their context window. Permissions and operational safeguards need to be enforced by the infrastructure, not left to instructions in a prompt. This is the part of context engineering I find most valuable: making organizational knowledge usable at the point where work happens. It is also shared infrastructure. Every engineer should not have to assemble the same integrations and explain the same conventions independently. At @aiDotEngineer world fair 2026, I shared how we approached this with CAPT at LinkedIn, bringing organizational context, tools, and workflows to off-the-shelf coding agents. #aiengineer# #agents# #coding# #contextengineering# #agenticmemory# #mcp# #playbooks# #skills#