Most AI agents perform well in demos but fail in production. Patterns that claim to be flexible often introduce new failure modes, leading to unpredictable systems that are harder to debug and maintain. It takes significant effort to build systems that behave consistently enough to earn trust, but only one bad output to lose confidence.

To move from prototypes to production systems, join us on May 7 to learn practical design patterns for building AI agents that actually work in the real world. See when single-agent systems suffice and when multi-agent orchestration adds value. Discover the common failure modes in agentic systems, such as context breakdown and tool misuse, and how to fix them.

You’ll also learn how graph-based context improves reasoning visibility and decision traceability in multi-step AI systems. Register now to design systems that are predictable, debuggable, and maintainable — and walk away with practical patterns you and your team can rely on.