incident_management · saas · workflow
LinkedIn's Contextual Agent Playbooks & Tools (CAPT) gives AI coding agents organizational context for 1,000+ engineers
AI coding agents lacked the organizational context needed to help LinkedIn engineers with company-specific systems, frameworks, and workflows, limiting them to generic coding tasks and leaving engineers hesitant to rely on them for real work.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Engineer submits natural language request
Engineers, PMs, and EMs start with a natural language question to initiate the workflow.
Tools used
Model Context Protocol (MCP)GitHub CopilotJinja2Trino
Outcome
CAPT is used by more than 1,000 LinkedIn engineers; issue triage time dropped by about 70%, data analysis is roughly three times faster, and over 500 playbooks have been authored company-wide.
What failed first
Existing AI coding assistants like GitHub Copilot handled generic tasks but broke down on LinkedIn-specific workflows, and an initial namespace-based approach to organizing CAPT tools still pushed complexity onto engineers who had to manually manage which namespaces to enable.
Results
Time savedabout 70%
Volumemore than 1,000
Grounding & classification
Source type: technical build writeup
38 fields verified against source quotes.
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