Agentic SDLC coordinates planning, coding, testing, review, and validation in one intelligent development workflow.
Translates plain-English feature prompts or engineering requirements into structured specs ready for automated decomposition.
Software development involves fragmented steps such as requirements, planning, coding, testing, review and debugging. Engineers constantly switch context between disjointed tools, leading to communication drops and slow release cycles.
Agentic SDLC coordinates these steps using specialized AI agents. From requirement ingestion to final runtime validation, agents work in unison while maintaining full state history and developer approval checkpoints.
Displaying the exact step execution sequence from raw prompt to validated release.
Natural language requirement ingestion and feature spec parsing.
Deconstructs requirements into ordered, granular tasks with dependencies.
Pause workflow for developer plan review, editing, and explicit sign-off.
Autonomous code generation across python files, modules, and tests.
Executes automated test suites, validates outputs, and records metrics.
Code audit for syntax, security flaws, design patterns, and edge cases.
Real-time telemetry collection for execution time, latency, and success status.
Spawns isolated runtime environments to confirm clean application startup.
Comprehensive feature suite designed for accuracy, safety, state persistence, and reliability.
Parses user prompts, extracts functional constraints, and formats specs for automated planning.
Deconstructs complex software requests into ordered, granular tasks with clear dependency graphs.
Synthesizes modular production source code and test files matching repository conventions.
Developer safety gate allowing complete review, editing, and approval of plans before execution.
Runs Pytest unit and integration test suites automatically to catch bugs and regressions.
Performs automated code audits evaluating syntax correctness, security, and architectural fit.
Spawns isolated runtime execution checks to verify application startup and execution soundness.
Manages state transitions, dependency ordering, and async real-time WebSocket communication.
Captures error tracebacks and automatically feeds diagnostic feedback back to agents for self-correction.
Stores complete state history, generated code artifacts, and execution metrics in SQLite database.
Diagram reflecting the actual codebase implementation across frontend, backend, orchestrator, and database.
Watch how AI agents generate code, execute test suites, and validate runtimes in real time.
Interactive workspace application is running locally. Click below to launch the live interactive multi-agent pipeline workspace.
Mentioning strictly technologies actually implemented in the Agentic SDLC repository.
Core language powering backend agents & tools
High-performance async web endpoints & WebSockets
Lightweight persistent state store for workflows
Google DeepMind intelligence models for agents
Non-blocking asynchronous task execution
Decoupled specialized agents per SDLC phase
State machine managing dependency loops & approvals
Open repository collaboration & version control
Creator & Lead Architect, Agentic SDLC
Building next-generation autonomous software development pipelines where multi-agent AI systems handle planning, code generation, testing, review, and runtime validation seamlessly.