Updated June 2026

Agentic Code Review and Pull Request Automation

Class Duration

7 hours of live training delivered over 1-2 days to accommodate your scheduling needs.

Student Prerequisites

Professional software development experience
Familiarity with GitHub Actions or GitLab CI
Basic experience with at least one AI coding assistant

Target Audience

Software engineers, tech leads, and platform/DevEx engineers who want to integrate AI agents into the pull request and code review workflow. Relevant for teams looking to accelerate reviews, reduce review burden on senior engineers, and improve code quality with AI-assisted review bots and PR automation. Teams pairing review automation with spec-first delivery can continue with Spec-Driven Development with GitHub Spec Kit, and those automating tests can add AI-Driven Test Generation and Maintenance.

Description

This course focuses on the intersection of AI agents and the pull request lifecycle. We cover how to configure and run AI code review agents (GitHub Copilot code review, Claude Code in CI, and open-source alternatives) on pull requests, how to design automated PR quality checks (style, logic, security, test coverage gaps), and how to build agentic pipelines that can suggest or apply fixes automatically. We also address the organizational and process design questions: when AI review complements human review, how to tune false-positive rates, and how to maintain human accountability in an AI-augmented review process.

Learning Outcomes

Configure GitHub Copilot code review and Claude Code review agents on pull request triggers.
Build a CI pipeline step that runs an AI code review and posts structured feedback as PR comments.
Design automated quality checks (security, style, complexity, test gap detection) using AI agents.
Implement an agentic "auto-fix" pipeline for low-risk issues (formatting, linting, simple refactors).
Define review policies that appropriately combine AI and human review for different change types.
Measure and tune the accuracy and noise level of AI review feedback.

Training Materials

Comprehensive courseware is distributed online at the start of class. All students receive a downloadable MP4 recording of the training.

Software Requirements

GitHub account with Actions enabled (or GitLab equivalent), API key for at least one frontier model, and a sample repository for lab use.

Training Topics

The AI-Augmented Review Process

Review bottlenecks AI can address
What AI does well in code review vs. what humans must own
Integrating AI review without undermining team culture

GitHub Copilot Code Review

Enabling Copilot code review on repositories
Review comment format and customization
Enterprise policy controls
Feedback quality and tuning

Claude Code and Custom Review Agents in CI

Triggering Claude Code on PR events via GitHub Actions
Structured review output: JSON comment schema
Posting AI review feedback as PR annotations
Scoping review focus areas via CLAUDE.md

Open-Source Review Agents

Overview: CodeRabbit, PR-Agent (CodiumAI), and alternatives
Self-hosting vs. SaaS review agents
Configuration and prompt customization

Automated Fix Pipelines

Safe categories for auto-fix: formatting, obvious lint, import cleanup
Agentic "suggest-and-commit" workflows
Human approval gates before auto-merge
Audit trail and rollback design

Policy and Process Design

Defining which PRs require human review regardless of AI score
Calibrating confidence thresholds and false positive management
Communicating AI review role to the team
Spec-as-source-of-truth: reviewing code against a GitHub Spec Kit specification (covered in depth in the dedicated Spec Kit course)
Accountability and audit requirements