As AI agents move to production, a critical gap has emerged. Many implementations stop at using the Model Context Protocol (MCP) for “tools,” treating it as just another external plugin. This misses the true power of the protocol: grounding.

To solve mission-critical problems, agents must go beyond simple tool calls. In this session, we’ll show how BKOAI’s Deep Search Agent performs deep root cause analysis (RCA) in the energy industry by leveraging the full spectrum of MCP. We’ll dive into our use cases, demonstrating how we use MCP resources and MCP prompts to reason with complex graph and SQL databases—the core systems of record.

In this session, we’ll:

• Unpack Deep Root Cause Analysis — Learn the challenges of performing RCA in the energy industry and why agents that only use “MCP tools” fail to find the true source of a problem.

• MCP: Beyond Tools to Resources & Prompts — We’ll address the common misconception head-on. MCP is not just about “tools.” We will differentiate between shallow tool-use and deep grounding via MCP resources (your core data systems) and MCP prompts (the reasoning frameworks that query them).

• The “Grounded” Deep Search Agent in Practice — See how BKOAI’s agent uses MCP prompts to intelligently query, analyze, and synthesize data from multiple MCP resources (like graph DBs and SQL logs) to build a complete picture of an event.

• Live Demo: Tracing the “Why” — Watch a real-world energy industry use case showing how the agent traces a complex failure from a high-level alert back to its specific root cause in the database.

You’ll leave with a practical understanding of how to leverage MCP resources and prompts to build agents that don’t just call tools, but truly reason with your core enterprise data to solve critical industrial problems.