Agentic AI systems are easy to prototype but difficult to operate in production. While LLMs have improved reasoning capabilities, the primary challenge has shifted from model intelligence to production engineering. Long-running execution, external tool invocation, human approvals, failure recovery, and governance become critical once AI agents interact with enterprise systems.

A typical AI agent reasons with an LLM, invokes external tools through MCP, and pauses for human approval before executing high-impact actions. In production, APIs fail, LLM providers enforce rate limits, workers restart, and workflows lose execution state. Without durable orchestration, workflows replay previous steps or duplicate side-effecting operations.

This session demonstrates how Temporal enables durable workflow orchestration through deterministic execution, workflow persistence, retries, Saga compensation, and asynchronous approvals. Drawing on production experience, I will present a reusable architecture for orchestrating LLMs, MCP tools, enterprise services, and governance checkpoints. Attendees will gain practical patterns for moving Agentic AI from experimentation to production.