Grounding AI Agents with Root Cause Analysis
Dr. Bin Liu Program Manger, Technology Manager , BKOAI,
Dr Bin Liu, AI Technology Manager at BKO AI, explored how AI agents can be made more reliable by grounding them in classic root cause analysis rather than leaving them to roam freely across tools and prompts. He reviewed the Model Context Protocol approach of combining tools, resources and prompts, then showed that without careful design this can still lead to token overload, drifting context and agents that lose track of the real problem they are meant to solve.
Liu introduced a dynamic context framework where a supervisor agent breaks complex tasks into smaller pieces, delegates them to specialist agents and keeps each agent’s memory focused on its own responsibilities. Instead of passing raw data around, agents share distilled insights, which keeps communication efficient and anchored in the task at hand.
Building on this, he connected the familiar five steps of root cause analysis with an agentic workflow that can search knowledge graphs, query databases, read technical documents and fuse sensor data to identify what is really going wrong in an industrial system. The result is an AI assistant that does not just answer questions but works through evidence, traces causal factors and recommends grounded actions.
