Building AI Agents with Red Hat: A Developer's Step-by-Step Guide
Introduction
The surge in agentic AI—autonomous software that perceives, reasons, and acts—is reshaping enterprise development. Red Hat has stepped up to provide the foundational infrastructure, offering new desktop and developer suite capabilities, skills bundles, and a rolling Linux release—all without extra usage charges or metering. This guide walks you through leveraging these Red Hat AI tools to move from experimentation to production, ensuring control, security, and scalability. Whether you're a seasoned developer or new to AI agents, these steps will help you build and deploy agents directly from your local environment.
What You Need
- Red Hat AI subscription (includes Red Hat Desktop and Advanced Developer Suite)
- A compatible OS: Linux (RHEL 9+), macOS, or Windows 10/11
- Podman Desktop (Red Hat build, commercially supported)
- Access to OpenShift clusters (local or remote) for deployment testing
- Python environment for using Trusted Libraries
- Familiarity with containers and AI agent frameworks (e.g., LangChain, AutoGPT)
Step-by-Step Guide to Developing AI Agents with Red Hat
Step 1: Set Up Red Hat Desktop and Podman Desktop
Install Red Hat Desktop, which is now generally available with commercial support. This environment includes the Red Hat build of Podman Desktop, a streamlined container management application. Download the installer from your Red Hat account and follow the setup wizard. Once installed, launch Podman Desktop and configure it to use Red Hat's hardened container images. This step gives you a local, secure foundation for building AI agents without needing cloud infrastructure upfront.
Step 2: Enable Isolated AI Agent Sandboxing
Inside Red Hat Desktop, activate the isolated AI agent sandboxing feature. This prevents your AI agents from performing unintended actions on the host OS—critical for safety during development. The sandbox uses lightweight containers that restrict file system access, network calls, and system calls. To enable it, open the Podman Desktop settings, navigate to 'AI Sandbox,' and toggle the isolation mode. Test by running a sample agent; confirm that it cannot access sensitive directories or execute dangerous commands.
Step 3: Integrate with OpenShift Dev Spaces
Red Hat's OpenShift Dev Spaces is a zero-configuration, cloud-based IDE. Connect your preferred coding assistant—AWS Kiro (in technical preview), Claude CLI, Microsoft Copilot, Cline, Continue, or Roo—by installing the corresponding plug-in. From the Dev Spaces dashboard, launch a new workspace and choose your assistant. This integration lets you use frontier or private models directly in your IDE while maintaining the security of Red Hat's hardened environment.
Step 4: Leverage Hardened Images and Trusted Libraries
Red Hat Desktop is built on Red Hat Hardened Images—curated, stripped-down container images scanned for security vulnerabilities. For Python development, use Red Hat Trusted Libraries, which offer pre-built packages vetted against OpenSSF standards. Include a Software Bill of Materials (SBOM) and cryptographic signatures to verify supply chain integrity. To use them, pull an image from the catalog: podman pull registry.redhat.io/hardened-python:latest. Then, set up a virtual environment with the trusted packages using pip install --trusted-host from the Red Hat repository.
Step 5: Connect to Local or Remote OpenShift Clusters for Unit Testing
With your agent code ready, connect to an OpenShift cluster for testing. From the Dev Spaces terminal or your local Podman Desktop, authenticate using oc login. Deploy a test agent pod using a YAML configuration that references your hardened image. Run unit tests that simulate agent actions in the isolated environment. This step ensures your agent works across scaling scenarios before production.
Step 6: Deploy Agents with the Rolling Linux Release
Finally, deploy your agents using Red Hat AI's latest rolling Linux release. There are no extra charges or metering for these tools—usage is unlimited. Use podman push to upload your agent image to a registry, then create a deployment on your production OpenShift cluster. Monitor through Red Hat's dashboards. As new updates roll out, the rolling release ensures your agent benefits from security patches and performance improvements without manual upgrades.
Tips for Success
- Start simple: Begin with a basic agent (e.g., a CLI-based RAG bot) to test the sandboxing and integration flows before building complex multi-agent systems.
- Use the sandbox aggressively: Test edge cases where an agent might try to access forbidden resources—the sandbox should fail safely, not crash the host.
- Leverage the community: Red Hat's Hardened Images and Trusted Libraries are backed by an active open-source ecosystem. Join forums and GitHub discussions for real-world tips.
- Automate unit tests: Integrate your OpenShift testing into a CI/CD pipeline using Red Hat's Jenkins plug-ins or Tekton pipelines. This catches regressions early.
- Stay updated: Since the rolling Linux release updates frequently, subscribe to Red Hat's changelog to adapt to new features that might simplify agent development.