Deep Agent Harness Automation System
A LangGraph-powered MCP server for infrastructure orchestration with autonomous subagents.
README
Deep Agent Harness Automation System
A LangGraph-powered MCP server for infrastructure orchestration with autonomous subagents.
Overview
Deep Agent Harness Automation System orchestrates complex infrastructure tasks by delegating to specialized subagents:
- iac-golden-architect: Terraform planning, validation, and module management
- container-workflow: Docker image optimization, security scanning, and registry management
- team-accelerator: Repository creation, pipeline setup, and team onboarding
Quick Start
# Clone the repository
git clone https://github.com/the-Lobbi/ai-deep-template-engine.git
cd ai-deep-template-engine
# Configure environment
cp .env.example .env
# Edit .env with your Harness credentials
# Run with Docker Compose
docker-compose up -d
Documentation
| Document | Description |
|---|---|
| Architecture | System design and workflow diagrams |
| Subagents | Specialized subagent capabilities |
| API Reference | MCP server API documentation |
Environment Configuration
Required environment variables:
HARNESS_ACCOUNT_ID=your_account_id
HARNESS_API_URL=https://app.harness.io/gateway
HARNESS_API_TOKEN=your_api_token
HARNESS_ORG_IDENTIFIER=default
HARNESS_PROJECT_IDENTIFIER=your_project
MCP_SERVER_HOST=0.0.0.0
MCP_SERVER_PORT=8000
Run the MCP server (consumes MCP_SERVER_HOST/MCP_SERVER_PORT via AgentConfig):
python -m deep_agent.mcp_runner
Development
# Install dependencies
pip install -e ".[dev]"
# Run tests
pytest
# Format code
black src/ tests/
ruff check --fix src/ tests/
# Type checking
mypy src/
Architecture
The Deep Agent uses LangGraph to orchestrate workflows across specialized subagents:
┌─────────────────────────────────────────────┐
│ Deep Agent Orchestrator │
├─────────────────────────────────────────────┤
│ ┌───────────────┐ ┌──────────────────┐ │
│ │ Analyze │→ │ Route to │ │
│ │ Task │ │ Subagent │ │
│ └───────────────┘ └──────────────────┘ │
│ │ │ │
│ ↓ ↓ │
│ ┌─────────────────────────────────────┐ │
│ │ Specialized Subagents │ │
│ │ • iac-golden-architect │ │
│ │ • container-workflow │ │
│ │ • team-accelerator │ │
│ └─────────────────────────────────────┘ │
└─────────────────────────────────────────────┘
Features
- Autonomous Orchestration: LangGraph-powered workflow management
- Harness Integration: Native Harness Code and Pipeline API support
- Multi-Subagent: Specialized agents for IaC, containers, and team acceleration
- MCP Server: Model Context Protocol server for AI integration
- Production Ready: Docker, Kubernetes, and Helm support
CI/CD Pipeline
Harness pipeline includes:
- Lint & Test (ruff, black, pytest)
- Build Docker image
- Deploy to Kubernetes (dev, staging, prod)
Team & Support
Owner: DevOps Engineering Team Slack: #deep-agent-support On-Call: PagerDuty rotation
License
Apache License 2.0 - see LICENSE for details.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.