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🤖 02 | Agentic DevSecOps & SDLC Orchestrator (agent-swe-ai)

"Software delivery is the first fully AI-consumable enterprise process."

📋 The Problem

Software development at the enterprise level is slow and risk-prone, not due to developer limits, but because of massive coordination overhead: * Manual Gates: Requirements analysis, code generation, linting, security scanning (SAST/DAST), and unit testing exist in disconnected silos. * Late-Stage Security: Vulnerabilities are caught during security reviews after development is completed, leading to massive rewrite cycles. * Context Loss: Handoffs between product managers (Jira), engineers (GitHub), and security auditors result in drift from original requirements.

📐 The Solution: agent-swe-ai

agent-swe-ai is an advanced multi-agent system that orchestrates the entire Software Delivery Lifecycle (SDLC). By treating software delivery as a series of structured gates with defined input/output criteria, a team of specialized, tool-using agents collaborates under a central supervisor to take a high-level requirement and produce secure, tested, and ready-to-deploy code.

graph TD
    User[Jira Feature Request] --> Supervisor[Supervisor Agent]

    subgraph Specialist Swarm
        Supervisor --> PM[Product Analyst Agent]
        Supervisor --> Architect[Architect Agent]
        Supervisor --> Dev[SWE Coding Agent]
        Supervisor --> Sec[DevSecOps Auditor]
        Supervisor --> QA[QA Test Designer]
    end

    PM -->|Decomposed Specs| Architect
    Architect -->|Design Specs| Dev
    Dev -->|Raw Code Blocks| Sec
    Sec -->|SAST/Vulnerability Check| QA
    QA -->|Generates & Executes Tests| Supervisor

    Supervisor -->|Pull Request + Audit Log| GitHub[GitHub Enterprise]

    style Specialist Swarm fill:#f5f5fc,stroke:#4f46e5,stroke-width:1px

⚡ Core Agentic Patterns & Capabilities

1. Supervisor / Specialist Swarm

Rather than using a single massive agent, the platform divides the SDLC into dedicated cognitive specialists: * Product Analyst Agent: Parses Jira tickets, decomposes requirements, and generates behavioral specifications. * Architect Agent: Validates technical patterns, designs interface schemas, and checks for style compliance. * SWE Coding Agent: Generates raw, high-quality code. * DevSecOps Auditor: Automatically reviews code using SAST patterns, checking for credential leaks, SQL injections, and dependency vulnerability. * QA Test Designer: Designs, structures, and executes unit and integration tests.

2. Multi-Agent Feedback Loops

If the DevSecOps Auditor detects a vulnerability, or the QA Agent reports a failing unit test, the supervisor routes the failures and execution traces back to the Coding Agent with specific correction instructions. The code is patched and re-scanned autonomously before any human sees it.

3. Native Enterprise Tool Integrations

Agents are empowered with secure API tools to interact directly with core engineering infrastructure: * Jira API: Fetching backlogs and updating issue states. * GitHub API: Creating feature branches, writing files, and opening audited Pull Requests. * SonarQube / Snyk: Triggering static scans.

🛠 Tech Stack

  • Orchestration: Python 3.11+, LangGraph (agent state management), LangChain
  • Upstream LLMs: Anthropic Claude 3.5 Sonnet (complex refactoring), OpenAI GPT-4o
  • Security & Auth: Snyk APIs, GitHub App integrations
  • Testing: PyTest, Docker (sandboxed test execution)

🔗 Repository

Explore the complete codebase and deployment instructions: * GitHub Repository: https://github.com/mailtotanvir/agent-swe-ai