Operationalizing Multiagent AI for Critical Enterprise Systems 

A few of the questions answered in this webinar

• Why do so many multi-agent AI pilots stall before reaching production?

  Getting an agentic workflow working in a demo is one thing — getting it deployed reliably at scale is another. Gartner's Tom Coshow points to a specific reliability gap between pilot and production, and what separates the organizations that break through it from the ones that stay stuck.

• Why does insurance underwriting need multiple AI agents instead of one large model?

  Underwriting isn't one decision — it's a chain of judgment calls, each requiring different expertise. The Hartford's Chief AI Underwriting Officer explains why that kind of layered process breaks down with a single model.

• What separates production-grade AI agents from systems that only work in demos?

  Two things tend to separate the two, according to Gartner's Tom Coshow: how carefully the data reaching each step is curated, and whether the workflow has a built-in way to catch and correct its own mistakes before escalating to a human. The panel breaks down what that verification pattern actually looks like in practice.

• Where should humans stay in the loop as AI agents take on more decisions?

  The usual framing — "when can we remove the human?" — may be the wrong question. The panel makes the case for asking something different.

• How does enterprise AI combine neural models with rules and regulations?

  Large language models are trained on general knowledge, not your company's compliance requirements. Dr. Alex Waibel explains the architecture question this raises.