25-35%
Planning time reduction
Expected reduction in ticket breakdown effort once acceptance criteria and agent routing are standardized.
AI-Assisted DeliveryEngineering WorkflowHuman Approval
Multi-agent workflow for turning requirements into safer engineering delivery
This is a product-oriented delivery system, not an academic AI experiment. It models how a technical lead could use role-specific agents to analyze Jira work, split frontend and backend scope, coordinate review and QA, and keep final approval with a human owner.
2x
Review coverage
Reviewer and QA stages are explicit, making acceptance criteria, edge cases, and regression checks harder to skip.
Lower risk
Human-controlled release
AI assists planning and verification, while final merge and deployment approval stays with the engineering lead.
Operating model
Designed for engineering productivity, review quality, and controlled modernization
The system is intended for teams modernizing ecommerce or SaaS codebases where tickets often touch multiple layers, context is distributed, and release risk needs visible checkpoints.
Requirement intake
Capture Jira ticket context, acceptance criteria, platform constraints, risk areas, and definition of done before any implementation starts.
Scope classification
Classify the work as frontend, backend, full-stack, QA-heavy, or architecture-sensitive so only the relevant specialist agents are activated.
Parallel execution plan
Split implementation into owned workstreams with explicit boundaries, expected file areas, review criteria, and rollback considerations.
Review and QA gates
Route changes through reviewer and QA agents for code quality, acceptance criteria coverage, regression checks, and release notes.
Human approval
Keep final approval with the engineering lead so AI improves delivery speed without bypassing risk management or accountability.
Architecture summary
A portable layer that can sit beside an existing codebase
The long-term direction is a reusable package that can be added to different repositories, read project context, ingest Jira tickets, and coordinate agent work without forcing the host application to change its product code.
LLM orchestrator
Analyzes the ticket, identifies work type, selects agents, creates the execution plan, and explains the reasoning before implementation begins.
Role-specific agents
Frontend, backend, reviewer, and QA agents operate with separate responsibilities so parallel work does not blur ownership.
Approval ledger
Human checkpoints capture scope approval, review approval, QA readiness, and release approval for enterprise auditability.