Deep Agent Harness Automation System
Provides Docker image optimization, security scanning, and registry management capabilities, enabling automated container workflows.
Enables Helm chart management and deployment to Kubernetes clusters as part of infrastructure orchestration.
Supports deployment and management of Kubernetes resources, with integration into CI/CD pipelines for staging and production environments.
Offers Terraform planning, validation, and module management for infrastructure-as-code automation.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Deep Agent Harness Automation Systemcreate a new Terraform module for AWS VPC"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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 -dDocumentation
Document | Description |
System design and workflow diagrams | |
Specialized subagent capabilities | |
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=8000Run the MCP server (consumes MCP_SERVER_HOST/MCP_SERVER_PORT via AgentConfig):
python -m deep_agent.mcp_runnerDevelopment
# 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.
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