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

  1. Investigate: inputs include the story brief, codebase context, and constraints. Outputs are findings, assumptions, risks, and impacted components.
  2. Plan: outputs include the step-by-step plan, file list, test plan, and rollout or rollback considerations.
  3. Implement: outputs include incremental changes with checkpoints such as build, compile, or tests.
  4. Self-review: outputs include review notes, checklist pass or fail, and suggested improvements.
  5. PR packaging: outputs include PR description, validation evidence, and risk notes.
  6. 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