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AI-Native Agentic Workflows
This talk demos delivering outcomes using custom code blending deterministic steps and AI reasoning, covering the full agent lifecycle and builder implementation.
The goal of any agent is to deliver an outcome. But most real-world outcomes require a mix of deterministic steps that have to take place reliably, and those that require single-step or multi-step reasoning.
I’ll walk through a demo of how our product delivers outcomes via custom code written to implement a mix of deterministic and AI steps, all via intuitive plain-English interfaces.
I’ll also talk about the full lifecycle of an agent, which includes handling errors and implementing modifications, and how we’ve implemented those in our builder.