How do you move AI agents from a developer laptop to a production environment safely? Discover how Red Hat AI balances security, governance, and observability for multi-agent systems. Moving AI models from the developer inner loop to the enterprise outer loop exposes critical gaps in tracking and model connectivity. In this technical deep dive from Red Hat Summit, Roberto Carratala, Principal AI Platform Architect at Red Hat, demonstrates the enterprise architecture needed to make autonomous setups production-ready. Learn how Red Hat AI uses Kagenti so you can bring your own agent, applies zero-trust flow with SPIFFE and SPIRE for workload identity, and establishes a secure gateway using the Model Context Protocol. Roberto also details how Models-as-a-Service powered by llm-d delivers full traceability in a private AI environment, while agent guardrails block biased or harmful responses.
What is the single biggest hurdle your team faces when attempting to move AI models or autonomous agents out of the developer loop and into a production environment? Let us know in the comments.
Explore how to scale and secure your AI capabilities:
👀 Don’t miss all the AI interactive demos from Red Hat Summit 2026 → https://interact.redhat.com/share/2EjdVF2d6c6awUPQUIxT
🚀 Operationalize AI agents with Red Hat AI → https://www.redhat.com/en/products/ai/agentic-ai
✨ Discover the latest innovations in Red Hat Enterprise Linux AI → https://www.redhat.com/en/topics/ai/agentops
🤖 Operationalizing "Bring Your Own Agent" on Red Hat AI → https://www.redhat.com/en/blog/operationalizing-bring-your-own-agent-red-hat-ai-openclaw-edition
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