Software used to work the same way every time. You wrote the rules, set the logic, and got back predictable outputs. When something broke, you found the bug and fixed it. Reliability was a byproduct of control.

AI has changed that contract. The same input can produce different results each time. Failure doesn’t announce itself with a 404. It shows up as a hallucination that looks credible, a model drift that no one caught, or a response that’s technically coherent but quietly wrong. For government agencies and nonprofit organizations, that’s not an abstract risk. It’s a trust problem.

Join Forum One for a practical session on how to build AI-enabled products and workflows your organization can actually depend on. Brian Graves, VP of Engineering at Forum One, will walk through the architecture and measurement practices that turn promising AI experiments into reliable, production-ready systems without sacrificing the speed and flexibility that make AI worth using in the first place.

In this session, we’ll cover:

• Why deterministic thinking fails in probabilistic systems, and what to replace it with
• How orchestration frameworks like n8n and Activepieces contain uncertainty without killing flexibility
• The metrics that actually matter for AI reliability: grounding, hallucination rates, and task success
• How to know when your AI system is ready for production, and when it isn’t