AgentsThe story, in brief
Presentation: Context Engineering at LinkedIn: How We Built an Organizational Context Layer for AI Agents with MCP
LinkedIn shipped a context layer for coding agents. The result: 20% productivity gain, zero reliability loss.
The KeyNews take
Why it matters
A production agent deployment case study showing how to overcome context limitations in large codebases — architectural pattern + measurable outcome that practitioners can adapt.
The key facts
5 to knowLinkedIn built Contextual Agent Playbooks and Tools on Model Context Protocol (MCP)
System delivers procedural memory, code search, and runbooks to coding agents
Reported 20% productivity boost with zero reliability loss
Targets AI agent limitations in large codebases
Includes operational guardrails and architectural details shared publicly
Go to the source
InfoQ AI/MLinfoq.com
Publisher excerpt: Ajay Prakash discusses how LinkedIn overcomes AI agent limitations in large codebases. He explains Contextual Agent Playbooks and Tools - built on Model Context Protocol (MCP) - which serves procedural memory, code search, and runbooks directly to coding agents. Prakash shares architectural details…