Ask any enterprise BI leader to describe their experience with AI for BI in one word and you'll hear it over and over: unimpressive. Business users try these systems, get shallow answers, and stop coming back. The problem isn't the model. The same class of LLM that powers Claude Code and OpenAI Codex sits behind most BI agents. The difference is the harness around it.
This piece breaks down what a great BI agent harness looks like, why the common approaches on the market fall short, and what it takes to match the work of a skilled human analyst:
- Anatomy of an AI Harness. The five-stage loop of Router, Planner, Executor, Evaluator, and Formatter that turns a model into a reliable analytics agent, and why tools are the ceiling on what the LLM can do.
- What a BI Harness Must Give the LLM. Certified reports across every BI tool, the semantic models behind them, cloud database agents for tables not yet covered by a report, and proactive KPI monitoring rather than reactive Q&A.
- Where the Obvious Approaches Fall Short. BI tool agents only see one dashboard's semantic model. Database agents can't see the reports users trust. Both lack a business glossary and proactive monitoring, so they compute the math but miss the meaning.
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