OSS + ELC EU 2026

Imagine asking an AI agent to analyze any data, only for it to hallucinate a schema that doesn't exist. This "context gap" is the primary barrier to reliable Data-Aware AI. While LLMs are brilliant reasoners, they are historically "blind" to the live, structured world of Postgres - until now!

In this talk, we will explore how the Model Context Protocol (MCP) bridges this gap, transforming Postgres from a passive storage layer into an active reasoning engine. We’ll dive into the architecture of a Postgres MCP server, demonstrating how it provides agents with secure, structured access to schemas and metadata. By utilizing tools like pg_catalog and EXPLAIN, we enable LLMs to understand table relationships and optimize performance before running queries.

Expect live demos and practical insights on making Postgres "talk" to AI safely and effectively.

Key Takeaways:

* How Postgres + MCP provides LLMs with correct, real-time database context.
* Leveraging Postgres metadata and schema to eliminate SQL hallucinationcs.
* Implementing secure, multi-tenant, and read-only access for AI agents.
* Enabling AI agents to use EXPLAIN and other tools for self-optimization.