Skip to content

Building AI Agents for OpenShift

A hands-on tutorial that takes you from zero to a deployed AI agent system on Red Hat OpenShift, using the fips-agents framework.

What you'll build

By the end of this tutorial, you'll have a complete system running on OpenShift:

Browser → Chat UI → Gateway → Agent → MCP Server (calculus tools)
                                  ↓
                              LLM (vLLM)
  • A Calculus Helper agent that solves math problems using remote tools
  • A calculus MCP server with 8 SymPy-powered tools (integration, differentiation, limits, etc.)
  • An HTTP gateway that proxies OpenAI-compatible API requests
  • A chat UI for browser-based interaction

Prerequisites

What you need

  • Python 3.11 or later
  • Access to an OpenShift cluster with a deployed LLM (vLLM or LlamaStack)
  • fips-agents CLI: pipx install fips-agents-cli
  • oc CLI: Install from Red Hat
  • helm CLI: Install from Helm
  • A terminal and a text editor

Modules

Module What you'll do
1. Scaffold Your Agent Create an agent project, explore every file
2. Configure and Deploy Edit config, deploy to OpenShift, verify
3. Build an MCP Server Create a calculus tool server from scratch
4. Wire MCP to Agent Connect the tools, update the prompt
5. Gateway and UI Deploy the full stack, test end-to-end
6. Code Execution Sandbox Deploy a sandbox, give the agent code execution
7. Extend with AI Use AI-assisted slash commands to add capabilities
8. Production Hardening Secrets, FIPS, scaling, monitoring

Reference

Deep-dive pages linked from the tutorial:

How to follow along

Each module builds on the previous one. You'll run real commands, edit real files, and deploy real services. The completed code is in this repository if you get stuck:

  • calculus-agent/ -- the finished agent
  • calculus-helper/ -- the finished MCP server