Did you know…
Starburst just wove “agentic AI” directly into its data-lakehouse stack, rolling out two headline capabilities: Starburst AI Workflows and an out-of-the-box Starburst AI Agent. AI Workflows knit together vector search, rich metadata, and governance so teams can move from experimentation to production in one governed plane; the accompanying AI Agent gives analysts and downstream LLM agents a natural-language interface that queries live data across clouds or on-prem without copying it. Early customers report 66 percent lower S3 storage spend after automated Iceberg table maintenance and cluster routing that steers queries to the most cost-effective engine.
Ok, So What?
For business leaders, the announcement lands at the intersection of three pain points: fragmented data, skyrocketing AI prototypes that never reach scale, and growing regulatory scrutiny. By embedding agents and workflow orchestration inside the lakehouse, Starburst bets that data gravity beats model gravity. In practical terms, this means:
- Lower latency for AI-powered experiences because agents run “lakeside” on governed data rather than brittle data extracts.
- A single control plane for governance and vector context, which simplifies compliance audits and shortens the time from POC to ROI.
- Reduced cloud spend from automated Iceberg optimizations, freeing budget for higher-value AI experimentation.
Now What – three project ideas you could pilot next quarter
- Self-service Insights Bot: Point the Starburst AI Agent at your finance and CRM sources; expose it in Slack so product managers can ask, “Which customer cohort has the fastest upsell velocity this month?” and receive SQL-backed answers in seconds—no analyst queue required.
- AI Feature Store Governance: Use AI Workflows to push real-time features (e.g. fraud scores, IoT sensor deltas) into a governed Iceberg table that both ML engineers and compliance teams can trace end-to-end, aligning with the MIT course’s “Trustworthy AI” pillar.
- Cost-aware Query Routing: Adopt Galaxy’s role-based routing to send low-priority BI dashboards to a cheaper compute pool and high-priority GenAI workloads to GPU-accelerated clusters; publish the savings as an OKR for your DataOps team.
Questions to think about
- Where in your current AI pipeline is data still moving to the model instead of the model moving to the data, and what risks does that pose?
- How might agentic AI change your data-governance operating model—do you have clear ownership for prompt engineering, vector security, and chargeback?
- If automated table maintenance can cut storage costs by two-thirds, which other “data-ops” tasks could you automate to fund innovation?