Enterprises struggling to move from copilots to autonomous agents are discovering that the bottleneck is the data foundation underneath it. Agents that can’t trust the metrics they’re reading, can’t enforce access policies at runtime, or can’t maintain consistent business definitions across tools create liability.
This Data Science Connect panel examines what production-grade agentic AI actually requires.
What This Session Covers
- What separates goal-directed agents from traditional LLM assistants
- The infrastructure requirements for persistent memory, feedback loops, and context retention
- Governance and observability strategies that work when AI is initiating action, not just responding
- How semantic context reduces hallucinations and keeps autonomous systems grounded in trusted business logic
- Integration patterns for connecting agentic systems to existing enterprise data ecosystems
What You’ll Walk Away With
- Clarity on what “agentic” actually means: Understand the core components, including reasoning, planning, and autonomous execution, and where most enterprise architectures fall short when they try to support them.
- A framework for enterprise readiness: The technical and organizational infrastructure required to deploy agentic systems at scale without sacrificing control.
- Practical governance design Guardrails, audit trails, and human-in-the-loop strategies for AI that initiates action.
Agentic AI lowers the barrier to executing decisions. That’s why the data foundation underneath it has to be right from the start.
