Most enterprises are still treating AI like a tool layered on top of existing systems. But shifting from AI as a passive assistant to a proactive agent workforce requires something bigger: a new operating model, a production-ready runtime, and full framework-agnostic control over how and where agents run.
Dropping general-purpose models into mission-critical workflows isn’t enough. This session breaks down the architectural evolution required to make agentic AI deliver real outcomes — without compromising governance, reliability, or flexibility.
What you’ll learn
- The three architectural requirements for enterprise-ready agentic AI
- What separates experimental agents from production-grade, mission-critical systems
- How to scale agents without sacrificing governance, control, or flexibility
- A practical framework to move your organization from AI pilots to real operational impact
Who you’ll hear from
Venky drives the definition and delivery of the DataRobot Enterprise AI Suite. Venky has twenty-five years of experience focusing on big data and AI as a product leader and technical consultant at top technology companies (Microsoft) and early-stage startups (Trilogy).
Rowan’s research focuses on AI, ML, and data science, looking at challenges and opportunities for technology executives and their teams. In his primary coverage area, he spearheads Forrester’s research on generative AI technologies, tools, and strategy. He also covers enterprise AI/ML platforms, cognitive search, and synthetic data.
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