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Agentic SDLC Adoption Guide Method

This method helps a software team bring agents into real delivery work while keeping the discipline that makes software reliable. The team starts with the product intent, architecture direction, risks, and acceptance path, then decides where agents can help.

Core Premise

Software engineering teams can use agents for code, tests, documentation, refactoring, analysis, and review. Durable value comes when engineers, architects, and technical leaders guide that work with clear intent, constraints, design, and verification.

Agent-assisted development raises the bar for human understanding. Before delegating work, the team needs to understand the domain, workflow, architecture, data, constraints, and intended outcome.

Requirements, design specs, architecture decision records, acceptance criteria, test plans, data models, interface contracts, and runbooks become control surfaces for human-agent collaboration. They tell the agent what problem is being solved, what may change, what must stay true, what tests must pass, and where human review is required.

A disciplined agentic approach starts with engineering intent and verification, then uses agents to accelerate analysis, implementation, tests, documentation, artifact maintenance, and repetitive work inside that frame. The team uses agents to move faster without letting the project drift away from the intended design.

Core Shape

  1. Humans define the problem, architecture, constraints, standards, and acceptance path.
  2. Engineering artifacts turn that intent into instructions both humans and agents can use.
  3. Agents execute bounded work inside the frame set by those artifacts.
  4. Humans review, verify, and decide whether the work is correct, secure, maintainable, and aligned with the business problem.
  5. Teams use agents to help keep artifacts, tests, and review gates current as the work changes, while humans stay responsible for direction, judgment, and acceptance.

Operating Principles

  1. Start from the product workflow, not the tool.
  2. Keep source authority visible.
  3. Give agents bounded tasks and reviewable outputs.
  4. Use agents to help maintain specs, tests, review gates, documentation, and handoff packets as the work changes.
  5. Keep secrets, private customer data, and regulated data out of prompts unless local policy allows that use.
  6. Make validation part of the workflow before scaling adoption.
  7. Keep final accountability with local owners.
  8. Use refresh loops because AI-assisted engineering practice changes quickly.

Selective Use

Teams do not need to apply the full method at once. Use the full sequence when the team needs an end-to-end adoption path. Use one section when the live question is narrower:

  • Use Start Here when the project needs an operating mode and human review boundary.
  • Use Operating Principles when the team needs shared architecture doctrine.
  • Use Project Intake when the project type, owner, risk class, or decision horizon is unclear.
  • Use Product Canvas when product intent, MVP scope, users, workflows, and non-goals need definition.
  • Use Prototype And Current-State Discovery when current workflow, permissions, data, evidence, or prototype disposition are unknown.
  • Use Workflow Mapping when states, actors, rules, exceptions, and UAT scenarios need to become explicit.
  • Use Domain And Data Model when entities, lifecycle, audit, retention, and migrations need review.
  • Use Architecture Options when agent roles, technical boundaries, tradeoffs, scoring, and revisit triggers need a decision.
  • Use Target Architecture when diagrams, ADRs, module maps, and implementation slices need to guide delivery.
  • Use Agentic SDLC when agent workflow, human gates, traceability, and documentation maintenance need operating rules.
  • Use Testing And Validation when tests, acceptance evidence, golden scenarios, or review gates need to improve.
  • Use Environments And Operations when deployment, promotion, rollback, logs, and runbooks need definition.
  • Use Security And Governance when access, data, production, or high-stakes risks are present.
  • Use Client Visibility when stakeholder artifacts, decision logs, demos, and known limitations need alignment.
  • Use Production Readiness when launch evidence, accepted risks, support ownership, and post-launch review need a gate.
  • Use Roadmap when the team needs a staged 30/60/90 path.
  • Use Templates And Worksheets when project artifacts need consistent forms.
  • Use Prompt Library when an agent needs a bounded work order.
  • Use Study Guide when the team wants the source foundation or refresh path.

Standard Flow

  1. Start here and operating principles.
  2. Project intake and product canvas.
  3. Prototype and current-state discovery.
  4. Workflow and domain/data mapping.
  5. Architecture option scoring and target architecture package.
  6. Agentic SDLC operating model.
  7. Testing, validation, environments, release path, and operations.
  8. Security, governance, client visibility, and production readiness.
  9. Roadmap, templates, prompt library, and source foundation.
  10. Agent handoff using exported state and this context packet.

Human Outputs

A team should leave the workbench with:

  • A written project profile.
  • Named local reviewers.
  • A product thesis and MVP/non-goal boundary.
  • A current-state discovery map.
  • Workflow, domain, and data model notes.
  • Architecture options, weighted scores, and tradeoffs.
  • Target architecture package outline.
  • A validation and evidence plan.
  • A security and governance checklist.
  • Production readiness scorecard.
  • A staged roadmap.
  • An exported project-state JSON packet.

Agent Outputs

An agent can help produce:

  • Intake brief.
  • Product canvas.
  • Discovery plan.
  • Architecture option comparison.
  • Target architecture package.
  • Test strategy.
  • Security and governance review checklist.
  • Production readiness review.
  • Roadmap and first-pilot plan.
  • Updates to specs, tests, review gates, documentation, and handoff packets.

Agents should not produce final approval for security, compliance, legal, financial, medical, safety, or production-release decisions.

Source Refresh

The source library is dated. A monthly heartbeat reviews public source changes and creates candidate updates. Treat heartbeat notes as review inputs, not automatic truth.