Agent-Friendly Data Platforms: Semantic Layers, Tool APIs, and Guardrails for Agentic
As Large Language Models evolve from passive chatbots to autonomous agents, the way they consume data is fundamentally changing. Traditional data platforms are built for human analysts—optimized for static dashboards, batch processing, and ad‑hoc read-only queries. But what happens when your primary data consumer is an autonomous agent that needs real-time context, semantic understanding, and the ability to take action?
This talk bridges traditional data engineering and the emerging needs of agentic AI. We’ll explore the architectural shifts required to build agent-friendly data platforms: robust semantic layers, data access via deterministic tool/function-calling interfaces, and strict guardrails so agents interact with data safely and predictably.
Key takeaways:
The fundamental differences between analytical and agentic data consumption
How to build semantic layers and tool-calling interfaces for autonomous agents
Best practices for auditing, rate-limiting, and securing agentic database interactions