The 4-Layer Production Stack Every AI Agent Needs

Gartner’s January 2026 research on Universal Orchestrators highlights a critical reality: AI agents lose value when deployed in isolation. To deliver real business outcomes, agents must operate within explicit, governed, and observable business processes.

Yet most enterprise teams are still building agents that work in demos but fail in production. The problem is rarely the model. It is the infrastructure underneath. Production agents require a runtime that can handle connectivity, orchestration, reliability, and governance at scale.

In this webinar, we break down the four-layer production stack that closes the gap between agent prototypes and enterprise-grade deployments.

You will learn how modern teams structure their AI infrastructure across four essential layers:

Make your APIs, microservices, and workflows agent-ready using an MCP Gateway. Instead of rewriting systems to become MCP compatible, existing services can be exposed as tools with minimal effort, allowing agents to orchestrate multiple APIs and workflows without replatforming your backend.

Rapidly compose and iterate on agentic workflows using prompt native tooling so teams can move from experimentation to deployment in minutes instead of months.

Use a proven orchestration runtime, originally created at Netflix, to provide agents with the durability, fault tolerance, state persistence, and horizontal scalability required for real production systems.

Gain full visibility into agent behavior with decision traceability, audit trails, cost monitoring, and access control across every action your agents take.

Whether you are an engineer deploying your first production agent or an architect designing infrastructure for enterprise scale AI systems, this session provides a practical blueprint for building agents that are reliable, governed, and ready for production