AI can generate a large pull request in minutes; a careful human review still takes time. This imbalance is turning review into a critical constraint on AI-assisted delivery. Simply adding another AI reviewer is not enough: automated comments can be noisy, miss organisational context or reinforce the assumptions made by the generating agent.
This course treats review as a layered quality system. Deterministic tools enforce rules that should not vary. AI reviewers investigate intent, behaviour and context. People remain responsible for architecture, risk and approval. Participants will learn to configure each layer, control false positives and require evidence rather than accepting fluent review commentary.
The result is a review workflow designed for useful scrutiny rather than maximum comment volume.
Learning Outcomes
Upon completion of this course, participants will be able to:
- Explain how agent-generated code changes review economics and risk
- Define review policy according to change type and materiality
- Combine static analysis, tests and AI review without duplicating noise
- Provide an AI reviewer with appropriate requirements and codebase context
- Evaluate review comments for accuracy, severity and usefulness
- Review large agent-generated changes using risk-based techniques
- Configure quality gates and escalation paths in CI/CD
- Measure review effectiveness, rework and developer understanding
Course Outline
Code Review in an Agentic SDLC
- Why faster generation creates a verification bottleneck
- Characteristic risks in agent-authored pull requests
- The limits of line-by-line human inspection
- The limits of model-generated review commentary
- Review as a socio-technical control rather than a single event
- Defining responsibility for final approval
Establishing Review Policy
- Classification by scope, data sensitivity and reversibility
- Required reviewers for architecture, security and domain logic
- Rules for generated code, tests and dependencies
- Maximum practical change size and decomposition requirements
- Evidence required before a pull request is reviewable
- Exceptions, emergency changes and retrospective review
The Deterministic Quality Layer
- Compilation, type checking, linting and formatting
- Unit, integration, contract and property-based testing
- Security, dependency, secret and licence scanning
- Code coverage and mutation testing as test-strength signals
- Architecture and policy checks in CI
- Blocking merges without asking AI to enforce deterministic rules
Contextual AI Review
- Supplying the issue, specification and acceptance criteria
- Providing architecture decisions and repository conventions
- Asking reviewers to investigate behaviour before commenting
- Reviewing across files, call paths and affected tests
- Identifying unsupported assumptions and hidden scope expansion
- Requiring file references and reproducible evidence
Reviewing for Maintainability and Reliability
- Duplication, unnecessary abstraction and cognitive debt
- Error handling, resource management and concurrency
- Boundary conditions, partial failure and data consistency
- Compatibility and migration concerns
- Operational visibility and supportability
- Distinguishing stylistic preference from material quality risk
Security-Focused Review
- Authentication, authorisation and trust-boundary changes
- Input validation, injection and unsafe deserialisation
- Secrets, sensitive data and logging exposure
- Dependency and generated configuration risks
- Agent-added network, filesystem and command execution
- Escalating findings that require specialist review
Controlling Noise and False Confidence
- Calibrating severity and confidence
- Deduplicating AI, static-analysis and human comments
- Sampling automated findings to measure precision
- Preventing automatic fixes from bypassing review
- Handling conflicting recommendations from reviewers
- Recognising a clean report as absence of evidence, not proof
Review Workflow and Metrics
- Triggering review at draft, ready and post-merge stages
- Agent-assisted summaries and change-impact maps
- Human review of high-risk decisions and generated evidence
- Measuring escaped defects, rework and review latency
- Tracking comment acceptance without rewarding comment volume
- Maintaining reviewer skills and codebase understanding
Practical Capstone
- Classify an agent-generated pull request by risk
- Configure deterministic quality and security gates
- Run a context-aware AI review
- Triage false positives and verify material findings
- Complete a focused human architectural review
- Produce an evidence-based merge or rejection decision
Intended audience
This course is designed for software developers, technical leads, test and quality engineers, application security professionals and engineering managers who review AI-assisted changes or establish organisation-wide review policy. It is applicable across languages and development platforms.
Prerequisites
Those attending this course should meet the following:
- Experience reviewing production code and pull requests
- Familiarity with automated testing and continuous integration
- Understanding of maintainability, reliability and secure coding principles
- Basic experience with AI-assisted software development