Workflow Blueprint
Agentic SDLC workflow
A tool-agnostic blueprint for designing agentic workflows in software delivery: how to
structure AI agents so they accelerate execution while preserving engineering ownership and quality.
Agentic phases
- Investigate: inputs include the story brief, codebase context, and constraints.
Outputs are findings, assumptions, risks, and impacted components.
- Plan: outputs include the step-by-step plan, file list, test plan, and rollout or
rollback considerations.
- Implement: outputs include incremental changes with checkpoints such as build,
compile, or tests.
- Self-review: outputs include review notes, checklist pass or fail, and suggested
improvements.
- PR packaging: outputs include PR description, validation evidence, and risk notes.
- Review iteration: outputs include targeted fixes responding to feedback with
minimal scope expansion.
Ownership model
- Agent: proposes and drafts.
- Engineer: validates, decides, and owns final output.
- Reviewer: enforces standards and checks for subtle drift.
Quality gates
- No plan means no code.
- No tests means no merge unless an exception is explicitly documented.
- Parity evidence is required for migrations.
- PR size and scope limits reduce review risk.
- High uncertainty triggers a stop condition and a human-first analysis path.
When not to use agents
- High-ambiguity requirements without a stable source of truth.
- Deep work in novel libraries where wrong assumptions are costly.
- Security-sensitive changes without an explicit review path.
- Large refactors that lack incremental safety nets.
Search topics
- agentic workflows
- agentic SDLC
- AI SDLC
- quality gates
- parity testing
- PR readiness
- human-in-the-loop