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Agentic SDLC Template: A Modular Governance Framework for AI-Assisted Software Development

This is a modular, agent-centric software development lifecycle governance template for AI-assisted workflows. It separates macro planning and micro execution via a dual-agent architecture, supporting test-driven development and large-scale PRD management.

AI辅助开发软件开发生命周期智能体架构测试驱动开发TDDClaude Code项目管理敏捷开发PRD管理
Published 2026-04-07 03:15Recent activity 2026-04-07 03:21Estimated read 8 min
Agentic SDLC Template: A Modular Governance Framework for AI-Assisted Software Development
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Section 01

Agentic SDLC Template: Introduction to the Modular Governance Framework for AI-Assisted Software Development

This article introduces the Agentic SDLC Template—a modular, agent-centric software development lifecycle governance template for AI-assisted workflows. Its core feature is a dual-agent architecture that separates macro planning and micro execution, supporting test-driven development (TDD) and large-scale PRD management. It aims to form an efficient collaboration paradigm between AI agents and human developers.

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Section 02

Project Background and Vision

As AI coding assistants become more capable, traditional software development processes are facing changes. However, many teams still use old models and fail to fully leverage AI's potential. The Agentic SDLC Template emerged as a response—it is not just a document template but a new collaboration paradigm, enabling dual-track collaboration between AI agents and human developers to fully unlock AI's value in development.

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Section 03

Core Dual-Agent Architecture Design

The template uses a dual-agent architecture to separate planning and execution:

Architect Agent (O Arquiteto)

Responsible for macro governance: high-level architecture design, tactical decisions (e.g., technology selection), cross-visual reviews, configured via GEMINI.md.

Operator Agent (O Operador)

Responsible for execution: CLI automation, TDD practices, system file management (e.g., CLAUDE.md), translating the architect's plans into runnable code.

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Section 04

Five-Phase Development Methodology

The template defines a five-phase creation cycle ( located at docs/agent-development-strategy.md):

  1. Project Initialization: Replace placeholders, configure AGENTS.md and GEMINI.md, and establish the agent fleet scenario.
  2. Roadmap Planning: PM defines feature epics in docs/roadmap-master.md.
  3. Blueprint Design: Create technical sprint blueprints using the @antigravity-micro-planner tool.
  4. CLI Programming: Sub-agents implement coding based on design interfaces.
  5. Continuous Iteration: Optimize via feedback loops to form a closed loop.
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Section 05

Core Advantage: Absolute Speed Breaking WIP Limits

The core value of the template lies in breaking WIP limits to achieve efficient development:

  • AI Data Bank Deletion: Agents maintain complete project memory, reducing information transfer loss.
  • Test-Driven Deployment: Automatically generate test cases, execute tests, fix issues, forming a quality assurance closed loop.
  • Large-Scale PRD Management: Architects control the PRD structure, while operators track specific implementations.
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Section 06

Practical Application Example: Microservice Backend Project Launch

Taking the new microservice backend project user-service as an example, the application process is as follows:

  1. Template Cloning and Configuration: Create the project using the GitHub template, replace the name, and configure agent roles.
  2. Agent Fleet Establishment: Define agents for architecture review, API design, test generation, etc., in AGENTS.md.
  3. Roadmap Planning: PM defines feature epics such as user registration and permission management.
  4. Blueprint Creation: Generate technical blueprints (API specifications, data models, etc.).
  5. AI-Assisted Development: Operator agents generate code skeletons, business logic, and tests.
  6. Continuous Delivery: Architects review, operators adjust, forming a continuous delivery pipeline.
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Section 07

Applicable Scenario Analysis and Ecosystem Expansion

Applicable Scenarios

  • MVP development for startups: Quickly validate concepts, with AI handling coding work.
  • Internal tool development: Efficiently complete repetitive business systems.
  • Technical debt cleanup: AI safely refactors legacy code.
  • Large-scale microservice migration: Architects control the overall process, operators migrate one by one.

Cautionary Scenarios

  • Highly innovative algorithm research: Requires deep participation of human experts.
  • Safety-critical systems: Strict manual review is needed for fields like aviation and healthcare.
  • Complex cross-system integration: Human architects lead coordination.

Ecosystem Integration and Expansion

  • Tool integration: Supports IDE plugins, Git workflows, CI/CD pipelines.
  • Expansion possibilities: Custom agents, multi-language adaptation, enterprise-level customization.
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Section 08

Summary and Future Outlook

The Agentic SDLC Template represents a new paradigm for AI-assisted development. Through the dual-agent separation design, it balances the advantages of AI code generation and the dominant role of human architectural decisions. The five-phase methodology provides clear guidance, and the modular design supports gradual adoption. As AI evolves, agent-driven development models will become more common, and this template provides a valuable reference implementation for the industry.

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