AI code review uses repository- or organization-specific agent skills, review instructions, coding standards, and read-only external context from connected systems such as issue trackers, documentation platforms, and service catalogs so pull request feedback reflects both local conventions and broader development context.
Engineers and reviewers still define the standards, create the instructions and connected context, decide which external sources and MCP servers to trust, and make the final judgment on code quality, risks, and merge readiness.
I help companies navigate AI adoption -- from strategy to production. Whether you are building your first LLM-powered feature or scaling an agentic system, I can help you get it right.
Design and build LLM-powered products and agentic systems
Go from idea to production with a clear implementation roadmap
Build AI with human-in-the-loop in regulated environments
Copilot code review support for agent skills and MCP servers is now generally available for all Copilot Pro, Pro+, Business, and Enterprise users. Previously announced in public preview, these capabilities let you bring your team’s tools, standards, and external context directly into every code review.