Insight Agents and the End of Dashboard-Driven Analytics

I just finished reading the Amazon Research paper “Insight Agents: An LLM-Based Multi-Agent System for Data Insights”¹ and this is the first time I have seen a truly production-minded attempt to close the gap between dashboards that show data and systems that actually help make decisions..

This is not a chatbot over a database.
This is not Text-to-SQL dressed up as AI.

This is a hierarchical multi-agent system that actually understands how business questions turn into data queries and then into insights.

What really stood out to me was the engineering discipline.

They did not use an LLM for everything.

• An auto-encoder handles intent detection in 0.009 seconds instead of 1.6 seconds.

• A fine-tuned BERT model handles routing in 0.3 seconds instead of ~2 seconds.

Only after this fast triage does the LLM come in for reasoning and narrative insight generation.

Small models for speed.
LLM for thinking.

That pattern alone is worth studying.

They also avoid naive Text-to-SQL. Instead, they use augmented querying with business context, APIs, and plan-and-execute workflows. The system breaks a question into steps, fetches the right data, and produces explanations that humans can actually act on.

In their evaluation, 90th percentile latency is under ~13 seconds with ~89% relevance and correctness judged by humans.

That is interactive. That is usable. That is very close to real.

The most important idea here is this:

You don’t explore dashboards anymore.

You ask questions to a system that already understands your schema, your metrics, your seasonality, and your business vocabulary.

And it tells you what happened and why.

This does not replace BI teams or data engineers.

It amplifies them.

It turns data infrastructure into a thinking layer for the business.

If you are building analytics platforms, GenAI agents, or data products, this paper is worth your time.

The future of analytics is not more charts.

It is conversational understanding over structured data.

References