AI systems don’t fail the way traditional software does.

When conventional software breaks, it usually tells you. You get an error message, a failed process, or something that clearly signals a problem. AI systems operate differently. Failure can appear as a confident but inaccurate response, a subtle drift in behavior over time, or an answer that sounds reasonable but isn’t actually grounded in reliable information.

For government agencies, nonprofits, and mission-driven organizations, that’s more than a technical issue. It’s a trust issue.

As AI moves from experimentation into real-world implementation, organizations need approaches that make systems more observable, measurable, and accountable without losing the flexibility that makes AI valuable in the first place.

In this recorded webinar, Brian Graves, Vice President of Engineering at Forum One, explores practical approaches for designing AI-enabled products and workflows organizations can rely on.

Watch the recording to learn:

• Why deterministic thinking breaks down in probabilistic systems
• The differences between traditional software and AI-enabled systems
• How orchestration frameworks can reduce risk while preserving flexibility
• Which metrics matter most for measuring AI reliability, including grounding, task success, and hallucination rates
• How observability helps teams understand and monitor AI behavior
• Practical approaches for determining whether an AI system is ready for production
• Real-world examples of trustworthy AI systems in practice

Building trustworthy AI systems starts long before implementation. If you’re exploring AI products, workflows, or organizational strategy, we’d love to continue the conversation.