Red Hat Summit 2026: What Separates Agent Pilots from Production Deployments
What Shipped
A production checklist for agent deployments: cryptographic identity, per-tool authorization and audit, sandboxed execution, deep tracing of every LLM call and tool use, and inference-aware autoscaling. These are the controls that turn an agent pilot into a production system.
What's Next
Evaluate whether your current agent pilots have identity, authorization, tracing, and autoscaling controls in place — or whether they are one spike away from a production incident.
Context
Red Hat AI framed the production gap explicitly: agent identity cannot be hardcoded API keys, dynamic code execution needs isolation and deep tracing, and multi-agent workloads break traditional infrastructure. These are architecture and operations concerns, not model concerns.
Decision
Build the AgentOps runtime before scaling agents. This means SPIFFE/SPIRE identity, MCP Gateway authorization, sandboxed execution, MLFlow tracing, and vLLM + LLM-d with inference-aware autoscaling — all on a platform the organization controls.
Outcome
Organizations that invest in the runtime get agents that are governable, observable, and scalable. The opportunity is not building the coolest agent; it is making agents runnable in a real enterprise.
The Judgment Call
The market is full of agent demos and wrapper products, but production-grade deployments are rare. The hard problems are runtime problems: who the agent is, what it can touch, where every decision is logged, and what happens when dozens of agents spike inference at once.
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