About this session
C-level leaders want to explore new metrics, but they need answers that are both meaningful and fresh. Can an AI agent answer a live business question when the data is changing, metrics have multiple definitions, and the latest events may not yet be visible?
This session presents a working Apache Doris pipeline for trustworthy real-time agent analytics. The goal is not only to generate a query, but to determine whether the result is semantically correct and current enough to trust.
The workflow is:
A user submits a free-text question. An LLM selects approved Doris MCP tools.
MetricFlow resolves the governed metric and dimensions, then Doris executes the compiled query.
Independent freshness and provenance checks determine whether the system should answer, request clarification, or block the response.
The implementation was validated with:
A 12-case evaluation matrix covering fresh metrics, product filters, churn, ambiguity, stale data, and wrong-metric attempts.
All expected outcomes passing against independent ground truth.
Complete traces containing prompts, model outputs, tool calls, compiled queries, execution results, freshness evidence, failures, latency, and cost.
The session then explains how this architecture is designed to scale toward billions of events on GCP and how experimental Row Binlog and Table Stream features documented for Doris 5.0 and later could provide more database-native freshness tracking than the current application-managed approach.
