[AI-ML SYSTEMS 2025] Zero to Production: Building Secure, ScalableMCP Servers and AI Agents with Open-Source Templates

Abstract

The gap between experimental AI agents and production-ready systems remains a critical challenge for enterprises adopting agentic solutions for software engineering with 95% of the GenAI pilots failing to make it to production. This hands-on tutorial demonstrates how applying rigorous software engineering practices to AI agent development can transform experimental prototypes into enterprise-ready systems that meet strict reliability, security, and performance requirements.

Event

5 International Conference on Al-ML Systems

Location

Bangalore, India

Description

Our tutorial presents two battle-tested, extensible templates (MCP and AI Agents) that have been developed and refined through real-world production deployments. These templates, openly available, provide a proven architectural foundation that accelerates the journey from concept to production while enforcing security best practices and operational excellence.

Participants will gain practical experience building a complete agentic ecosystem comprising: Part 1: MCP Server Development - Using our open-source template-mcp-server repository, attendees will create robust MCP servers that enable AI agents to interact securely with external systems. The template includes FastAPI-based HTTP servers, modular tool systems, comprehensive testing frameworks, and enterprise deployment configurations supporting OpenShift/Kubernetes environments. Part 2: Agent Implementation - Leveraging our template-agent framework, participants will build production-ready conversational agents with real-time streaming capabilities, multi-turn conversation management, and enterprise integration features including SSO authentication, PostgreSQL persistence, and Langfuse observability.

Key Technical Contributions

Rapid Deployment Framework: Automation scripts that transform base templates into domain-specific implementations, reducing development time from weeks to hours
Security-First Architecture: Rootless containers using Red Hat UBI, comprehensive authentication patterns, and secure tool execution environments
Production Observability: Built-in tracing, logging, and monitoring capabilities essential for maintaining agents in production
Universal Compatibility: Tool-first design ensuring seamless integration with LangGraph, CrewAI, FastMCP, and other major agent frameworks
Enterprise-Ready Features: Session management, checkpointing, error recovery, and scalable deployment patterns tested in production environments

Practical Outcomes

Each participant will complete the tutorial with:

A fully functional MCP server with custom tools deployed to a container platform
A streaming AI agent with enterprise authentication and conversation persistence
Access to reusable template repositories with comprehensive documentation
Automation scripts for rapid customization and deployment
Best practices documentation for maintaining agentic systems in production

By providing open-source, extensible templates rather than rigid frameworks, we enable teams to rapidly prototype while maintaining production standards, significantly accelerating the adoption of agentic solutions in software engineering workflows.

Open Source Commitment

Both templates are actively maintained and openly available:

MCP Server Template: https://github.com/redhat-data-and-ai/template-mcp-server
Agent Template: https://github.com/redhat-data-and-ai/template-agent

These repositories include comprehensive documentation, example implementations, and deployment manifests, enabling participants to immediately apply tutorial learnings in their organizations.

Last updated on Nov 10, 2025