Antigravity-SDLC: A Production-Level AI-Driven Software Delivery System

Antigravity-SDLC is an open-source production-level AI-driven software development lifecycle (SDLC) system. It integrates 13 intelligent agents and 2 human approval nodes to automate the entire process from requirement to deployment while retaining human control at key decision points. This system embodies the "human-in-the-loop" design philosophy, balancing AI efficiency with human oversight.

Traditional software development involves a large number of repetitive tasks and cross-team collaboration, which are time-consuming and error-prone. With the improvement of large language model capabilities, more developers are exploring how to integrate AI agents into the development process. However, fully automated software development still faces challenges: how to leverage AI efficiency while ensuring human oversight on key decisions? How to design a scalable and auditable agent collaboration system?

Antigravity-SDLC is an open-source project developed by Prateek Raj Aswal. Its core features include:

• Two human checkpoints: Set at the planning stage and final code review stage
• 13 specialized AI agents: Each responsible for specific tasks such as planning, architecture, backend development, frontend development, QA testing, security scanning, etc.
• Fully autonomous execution: Agents can collaborate independently between the two approval nodes
• Kanban management: Built-in Kanban board system to track project progress in real time

Antigravity-SDLC divides the software delivery process into six main stages:

1. Planning (human-supervised)

User provides product idea → Planner agent converts to structured requirements → Grill Me agent conducts adversarial review → Refine agent optimizes the plan → Human approval node (user reviews and approves before proceeding)

2. Architecture Design

Architect agent generates complete system architecture → Artifact Parser parses design into structured API contracts, database Schema and event definitions ###3. Execution (fully autonomous) Parallel tasks: database migration, backend development, frontend development, QA planning, code review, QA cycle, security scan, Kanban update ###4. Deployment DevOps agent configures deployment environment and CI/CD pipeline → code review → security scan ###5. Final Review (human-supervised) Human Review Agent summarizes deliverables into a review report → Human approval node (user confirms whether to launch) ###6. Document Generation Doc Writer agent automatically generates README, API documentation, Architecture Decision Records (ADR) and operation manual

Usage: Users need to create three configuration files in the project context:

• context/product.md: Describes product goals and user groups
• context/tech-stack.md: Specifies the technology stack
• context/constraints.md: Defines budget, timeline and compliance requirements Then call the Planner agent to start the full process: @planner Run the full_pipeline with idea: "Build a task management app with real-time collaboration..." It also supports stage-specific execution (e.g., @planner Run idea_to_plan, @grill_me Review this plan)

Technical Highlights:

• State machine-driven agent collaboration: Each agent has clear input, output and state transition rules, making the system scalable
• Memory storage mechanism: Built-in multiple memory stores to ensure cross-stage information continuity
• Human-AI collaboration balance: Retains human control at key decision points, avoiding "black box" risks

Application Scenarios:

• Fast prototype development: From idea to runnable prototype with a few interactions
• Standardized project initiation: Ensures new projects follow consistent technical specifications and documentation standards
• Full-stack application development: Supports end-to-end delivery of frontend, backend, database and deployment
• Legacy system transformation: Manages complex migration through clear stage division

Limitations:

• Generated code may need manual fine-tuning to meet specific business needs
• For highly customized or innovative projects, AI-generated architecture may require major adjustments
• Security-sensitive production environments still need final review by professional security engineers

Antigravity-SDLC represents a new software development paradigm: instead of replacing human developers, it uses a well-designed collaboration framework to let AI handle repetitive and standardized work, while humans focus on creative decisions and quality control. As large language model capabilities continue to improve, such "human-AI collaboration" development tools are expected to become standard practices in the software engineering field.