It's how to build systems that don't let hallucinations reach production.
Most AI agent platforms rely on prompting techniques to reduce hallucinations. They add guardrails, fine-tune models and hope for the best. In production, this means your AI agent will occasionally invent a policy, approve a refund you don't offer or promise something you can't deliver.
For customer-facing systems, that's not acceptable. The solution isn't better prompts. It's separating natural language understanding from business logic execution.
In this technical session, Michał Partyka (CTO, Zowie) walks through the architecture that eliminates hallucinations in business decisions.
You'll see how process-based execution keeps AI grounded while maintaining natural, conversational interactions. No vague promises, just the system design that works in production at enterprise scale.
Tomek Cieciuch (Solutions Consultant, Zowie) addresses the technical evaluation questions he hears from CTOs and engineering leaders every day. The questions that determine whether you can actually deploy AI agents in production or just run another pilot that goes nowhere.
You'll learn:
- How to architect AI agents that can't hallucinate on business logic (while still feeling natural to customers)
- Why process-based execution succeeds where prompt engineering fails at scale
- The technical difference between conversational AI (ChatGPT-style) and AI that executes business processes reliably
- How to evaluate AI agent platforms beyond the demo (the questions vendors hope you don't ask)
- Real production constraints from enterprise deployments handling millions of conversations
- What LLM-agnostic architecture means for your platform flexibility and vendor independence
Who should attend:
- CTOs and technical leaders evaluating AI agent platforms
- Product managers building AI-powered customer experiences
- Engineering leaders responsible for AI implementation and production reliability
- Solution architects assessing AI vendors for enterprise deployment
- Anyone who needs to understand what actually works in production vs. what works in demos
