Red Hat
The enterprise AI landscape is rapidly shifting from simple chatbots running on LLMs to agentic AI; autonomous systems capable of reasoning, planning, and executing complex, multi-step tasks. While adoption is skyrocketing—with Gartner predicting 40% of enterprise applications will feature task-specific agents by 2026—most organizations remain stuck in the pilot phase. The challenge is the "Production Gap": the massive delta between an agent that works on a developer's laptop and one that runs securely, at scale, and with full audit trails and compliance in a data center.
In this webinar, we explore how Red Hat AI "connects the dots" to bridge this gap. We will move beyond the hype of framework selection to focus on AgentOps—the essential production infrastructure required for enterprise-grade autonomous action. Join our experts as we dive into a full-stack, "Metal to Agents" approach that secures the entire AI lifecycle, starting at the Linux kernel and extending to the agent runtime.
Key takeaways:
- Run securely: Zero-Trust for autonomous actions. Learn how to protect shared infrastructure from "rogue" agents using kernel-isolated sandboxes that contain dynamic code execution within secure boundaries. We will discuss how to establish cryptographic identities for every agent using SPIFFE/SPIRE, ensuring least-privilege access to sensitive tools and data.
- Run reliably: Framework-Agnostic AgentOps. Discover how a Bring Your Own Agent (BYOA) strategy allows your teams to use the frameworks they prefer—like LangChain, OpenClaw, or CrewAI—while inheriting centralized lifecycle management and security. Understand the importance of deep execution tracing and observability to debug unpredictable agent behaviors and ensure mission-critical reliability.
- Run at scale: Optimized inference and tooling. Learn how to manage the complexity of scaling agentic workflows, even as multi-agent deployments generate unpredictable, concurrent spikes in memory and inferencing. See how Red Hat AI integrates vLLM and llm-d to provide high-throughput inference and intelligent scaling. We will also showcase how the Model Context Protocol (MCP) gateway serves as a universal adapter to securely connect agents to enterprise SaaS APIs and databases without custom-code maintenance.