Stop Optimizing Tokens. Start Fixing Your AI Architecture.(US)

The obsession with cheaper models, token optimization, and prompt engineering is a good first step. But what if the biggest driver of your AI costs isn't the model you're using, but the architecture you've built around it?

As enterprises scale AI agents, poorly designed architectures create runaway costs: unnecessary reasoning loops, redundant model calls, excessive tool invocations, duplicated work, and agents operating without clear execution boundaries. Switching to cheaper models won't fix an architecture that wastes resources by design.

AI cost efficiency must be architected, not retrofitted.

In this session, we'll challenge conventional thinking about AI cost optimization and explore the architectural principles behind building cost-efficient enterprise AI systems. Learn how to design agents that use the right models, tools, context, and execution patterns for each task—and how the Workato AI Control Plane provides the enterprise-wide visibility, policies, budgets, and guardrails needed to govern AI consumption across your ecosystem.

We'll demonstrate how the right architecture, combined with centralized cost governance, can turn unpredictable AI spending into measurable, sustainable business value.

You don't control AI costs by buying cheaper intelligence. You control them by architecting how intelligence gets used.

What you will learn:

• Why agent architecture drives AI costs
• Design agents for cost-efficient execution at scale
• Eliminate unnecessary model calls and tool executions
• Gain visibility into enterprise-wide AI spending
• Enforce budgets, policies, and consumption guardrails