quality_assurance · saas · workflow
LinkedIn platform team enables thousands of engineers with enterprise-scale background and foreground AI agents using MCP and sandboxed PR workflows
LinkedIn's engineering teams were experimenting with AI in silos, each reinventing the same infrastructure for prompt orchestration, data access, safety evals, and deployment, producing inconsistent proofs of concept unable to ship to production at scale.
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 · Developer writes task spec
A developer writes a spec expressing what to change, how to break the work down, which tools to allow, and what acceptance criteria define success.
Tools used
MCPGitHub Copilot
Outcome
LinkedIn built a unified agentic platform serving thousands of developers daily, with background and foreground agents sharing MCP tools, spec-driven task execution in secure sandboxes, PR-based human review, and reduction of engineering toil.
Results
Volumethousands of developers who are using these tools every single day
Grounding & classification
Source type: technical build writeup
26 fields verified against source quotes.
agent assistagentic workflowcode generationmulti agent workflowragcode diff prknowledge basehuman review describednamed customerproduction runtime claimedtools describedworkflow describedsoftwareemployee productivitytime savedtechnical build writeupincident managementquality assuranceagentic task executionai draft human approvalhuman review queue