Starburst

"Scaling R&D with agents" was the theme of Starburst engineering offsite in Warsaw last week. We left with 61 of them. Every team took part, including the ones that do not write code for a living. The agents do unglamorous work: triage a failed CI run, answer a cloud cost question, tell you which engineer knows a corner of the codebase, draft a response to a customer security questionnaire, find the feature flags nobody has cleaned up in two years. Three things I did not expect. 1. The platform mattered more than the agents: The most durable output of the week was not any single agent. It was the layer underneath: --- agent memory that survives between runs, --- a shared store so agents build on each other's knowledge, --- native scheduling, per-turn identity, and --- guards that decide what an agent may actually do. Other people's agents were rebuilt on those primitives within days. Nebula, our internal agent platform, hardened over this week. 2. Narrow beats clever: Every agent is scoped to one job and is read-only or draft-only. It flags, drafts and proposes, and a human lands the change. We enforce that in the platform rather than trusting a prompt to behave. 3. Silence is a feature: One of mine watches a customer-facing surface on a schedule and says nothing at all when everything is fine. It re-tests a suspected failure several times before it will interrupt anyone. A monitor that only speaks when it is sure is a monitor people still read six months later (no alert fatigue). The hard part was never getting a model to do something useful. It was permissions, scheduling, state, identity, and knowing when not to interrupt someone. That is ordinary platform engineering, and it is where our effort is focused. If you are building agents for real work rather than for a demo: what broke first for you?

There's so much more to your business than just data. Analysts rely on nuanced knowledge that AI lacks. Our latest blog discusses the importance of business context—how the right rules and definitions ensure accurate answers. As AI grows in analytics, clear context is vital. Discover how data products and a governance layer can help. Read more: https://okt.to/6jtXJa #AgenticAI #DataProducts #ContextEngineering #Starburst #BusinessContext #EnterpriseAI

Building a data product is just the beginning. The real challenge lies in ensuring it meets business needs. @Monica Miller presents a practical framework for testing and fixing AI data products. This involves collaborating with subject-matter experts to align product logic with business logic. Key focus areas include: ✅ Product metadata and business rules ✅ Column descriptions and definitions ✅ Underlying view logic These practices foster a reliable testing process for confident data product deployment. For those developing data products, this guide is essential for the post-build phase. Read the full post: https://okt.to/tY2qHA #AgenticAI #Starburst #DataEngineering #DataProducts #EnterpriseAI #DataQuality #DataGovernance

I’m happy to announce one of our largest, most audacious partnerships to date. Lando Norris is now the official brand ambassador of Starburst. Racing at the highest levels of motorsport, milliseconds are the difference between winning and losing. Drivers and teams don't have time to wait for data — they need it instantly, accurately, and from wherever it lives. That's exactly the problem Starburst was built to solve for the world's most demanding organizations. We're thrilled to welcome Lando Norris to the Starburst family. Just like Lando trusts split-second data to make race-winning decisions on the track, our customers trust Starburst to deliver the right data, at the right time, without compromise. Stay tuned — this is just the beginning of what we've got planned together.

Over the past month, I’ve met with enterprise AI leaders across four regions—Australia, Europe, India, and North America. The most striking takeaway? It’s not how different their AI strategies are. It’s how remarkably consistent their main bottleneck has become. Everyone is building AI agents. But almost everyone is struggling to give them trusted, reusable business context. Without bringing together distributed data, metadata, business logic, and governance, AI simply can't deliver reliable outcomes across a hybrid enterprise. This is the next major architectural hurdle for enterprise AI. At Starburst, we’re tackling this head-on with a business-facing context layer—enabling trusted enterprise intelligence without forcing another massive data migration. Looking forward to diving deeper into this tomorrow evening in Alpharetta with local AI leaders and Rysun Labs!

What happens when intelligence becomes free? According to a recent paper from @UC Berkeley's EPIC Data Lab, the next great challenge won't be generating intelligence. It will be managing it. In this new post, @Daniel Abadi explores the Berkeley team’s vision for the future of agentic AI and connects it to the emerging database architectures that agents will require. The paper argues that future data systems must do far more than answer queries. They will need to help agents: ✅ Maintain long-running state ✅ Coordinate with other agents ✅ Recover from failures ✅ Accumulate experience ✅ Continuously improve It's a compelling vision, and one that closely aligns with how we think about enterprise AI at Starburst. As agents become long-lived participants in enterprise workflows, they will increasingly depend on governed access to data, business context, and shared knowledge. Those capabilities don't emerge from the model alone. Instead, they emerge from the data foundation underneath it. That's why we've invested in capabilities like the Enterprise Context Layer, AIDA, governed data products, and the agentic control plane. Together, they provide the trusted foundation agents need to reason, collaborate, and act within real enterprise environments. Read the full article: https://okt.to/SW9CqL #EnterpriseAI #AgenticAI #DataArchitecture #AI #DataSystems #ContextEngineering

AI stalls when business context is fragmented, definitions are scattered, and the data behind an answer is hard to trust. In our upcoming webinar, Senior Product Manager Monica Miller will show how teams can use AI to build governed, trusted data products faster, creating the shared foundation that helps teams ask better questions, know the answers are grounded in the right definitions, and move faster from insight to action. This session connects that foundation to Starburst’s direction toward context-aware data platforms, where governed data products become the interface layer for dashboards, assistants, and agentic workflows. Monica will also share a behind-the-scenes look at how Starburst applied this approach internally during its own AIDA rollout. You'll learn: ✓ Why AI needs business context, not just data access, to return trustworthy answers ✓ How AI can accelerate data product creation and documentation ✓ What role business context plays in improving AI outputs ✓ How governed data products create a scalable, auditable foundation for enterprise AI Save your spot: https://okt.to/jwSmrT #DataProducts #AI #DataGovernance #Claude #EnterpriseAI

🥼 Starburst Lakehouse Lab is back for Episode 3. Hosted by Lester Martin 🥑 Lakehouse Lab is our educational YouTube series designed for data engineers, architects, and technical data professionals. Each episode takes a practical concept from the modern data stack and breaks it down into a short, hands-on lesson. This week, Lester tackles a deceptively simple question: What is business context related to AI? Is it the same thing as a semantic layer? Give Episode 3 a watch below. #DataEngineering #AI #SemanticLayer

One of the things I love about the Gartner Data & Analytics Summit is the quality of the data leadership conversations. These are the people making the calls on Al strategy, data governance and platform investment - and getting that right matters. At Starburst my role is helping organizations bridge the gap between Al ambition and data reality. Most enterprises have more data than they can effectively use. The architecture question - how you make that data accessible, governed and Al-ready - is where we spend most of our time. I'll be at Booth 410 in Sydney on 16-17 June. If you're a CDO, Head of Data or Al leader thinking through your data foundation strategy, let's connect. • Booth 410 | ICC Sydney I June 16-17 https://lnkd.in/ebCUSsYn ac/data-analytics-australia #GartnerDA #ChiefDataOfficer #AIStrategy #DataGovernance #Starburst