AI-Driven Development Harness
End-to-End Software Delivery Automation with 7 Specialized Agents
Pipeline at a glance
Situation
A development team needed to ensure AI-generated code consistently met specifications across a multi-repository codebase. Manual code review was catching spec deviations too late, and the feedback loop between ticket creation and verified implementation was slow and error-prone.
Task
Design an end-to-end development automation system that decomposes user stories into implementable subtasks, implements code using test-driven development, reviews it from both product and technical perspectives, and verifies the complete implementation matches the original user story.
Actions
Stage 1: Story Decomposition
Automated story analysis with triage agent evaluating relevance across all repositories.
- Sub-agents investigate each repository in parallel — analyzing file structure, existing patterns, type definitions
- Impact analysis agent identifies affected systems and presents implementation options
- Engineer approves approach before subtask breakdown begins
- Generates structured subtask descriptions using separate front-end and back-end templates
Stage 2: TDD Implementation
Per-subtask implementation with strict RED → GREEN → REFACTOR quality gates.
- Test case design agent derives test cases organized by perspective (API, mapping, interactions, errors)
- Implementation agent writes production code within strict scope constraints
- Refactoring agent improves code quality while maintaining GREEN state
- Automated scope verification via diff analysis prevents unintended changes
Stage 3: Dual-Track Review
Parallel review by two specialized agents covering spec compliance and code quality.
- Spec review agent: specification consistency, interface requirements, prohibited-change violations
- Tech review agent: security, coding standards, performance, existing code impact
- Results consolidated into unified report with severity levels (CRITICAL / WARNING / INFO)
Stage 4: Story-Level Verification
Cross-functional verification after all subtasks complete.
- Story verification agent checks user story achievement, inter-subtask consistency, front-end/back-end alignment
- Full test suite execution with implementation summary generation
- Engineer performs manual testing before final approval
Key Design Decisions
- Human-in-the-loop at every critical gate — AI does not proceed without engineer approval
- File-based communication between agents — enabling auditability and reproducibility
- Project management tool is read-only for AI — all write operations performed by engineer
Results
- Code output consistently meets specifications through the RED → GREEN → REFACTOR cycle
- Spec deviations caught during implementation rather than during manual review
- Dual-track review covers both "does it match the spec?" and "is the code good?" simultaneously
- Complete harness delivered from requirements gathering to production deployment