Summary
In this episode of the Data Engineering Podcast, Lucas Thelosen and Drew Gilson, co-founders of Gravity, discuss their vision for agentic analytics in the enterprise, enabled by semantic layers and broader context engineering. They share their journey from Looker and Google to building Orion, an AI analyst that combines data semantics with rich business context to deliver trustworthy and actionable insights. Lucas and Drew explain how Orion uses governed, role-specific "custom agents" to drive analysis, recommendations, and proactive preparation for meetings, while maintaining accuracy, lineage transparency, and human-in-the-loop feedback. The conversation covers evolving views on semantic layers, agent memory, retrieval, and operating across messy data, multiple warehouses, and external context like documents and weather. They emphasize the importance of trust, governance, and the path to AI coworkers that act as reliable colleagues. Lucas and Drew also share field stories from public companies where Orion has surfaced board-level issues, accelerated executive prep with last-minute research, and revealed how BI investments are actually used, highlighting a shift from static dashboards to dynamic, dialog-driven decisions. They stress the need for accessible (non-proprietary) models, managing context and technical debt over time, and focusing on business actions - not just metrics - to unlock real ROI.
Announcements
Interview
Contact Info
Parting Question
Closing Announcements
Links
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
In this episode of the Data Engineering Podcast, Lucas Thelosen and Drew Gilson, co-founders of Gravity, discuss their vision for agentic analytics in the enterprise, enabled by semantic layers and broader context engineering. They share their journey from Looker and Google to building Orion, an AI analyst that combines data semantics with rich business context to deliver trustworthy and actionable insights. Lucas and Drew explain how Orion uses governed, role-specific "custom agents" to drive analysis, recommendations, and proactive preparation for meetings, while maintaining accuracy, lineage transparency, and human-in-the-loop feedback. The conversation covers evolving views on semantic layers, agent memory, retrieval, and operating across messy data, multiple warehouses, and external context like documents and weather. They emphasize the importance of trust, governance, and the path to AI coworkers that act as reliable colleagues. Lucas and Drew also share field stories from public companies where Orion has surfaced board-level issues, accelerated executive prep with last-minute research, and revealed how BI investments are actually used, highlighting a shift from static dashboards to dynamic, dialog-driven decisions. They stress the need for accessible (non-proprietary) models, managing context and technical debt over time, and focusing on business actions - not just metrics - to unlock real ROI.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- If you lead a data team, you know this pain: Every department needs dashboards, reports, custom views, and they all come to you. So you're either the bottleneck slowing everyone down, or you're spending all your time building one-off tools instead of doing actual data work. Retool gives you a way to break that cycle. Their platform lets people build custom apps on your company data—while keeping it all secure. Type a prompt like 'Build me a self-service reporting tool that lets teams query customer metrics from Databricks—and they get a production-ready app with the permissions and governance built in. They can self-serve, and you get your time back. It's data democratization without the chaos. Check out Retool at dataengineeringpodcast.com/retool today and see how other data teams are scaling self-service. Because let's be honest—we all need to Retool how we handle data requests.
- Your host is Tobias Macey and today I'm interviewing Lucas Thelosen and Drew Gilson about the application of semantic layers to context engineering for agentic analytics
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you start by digging into the practical elements of what is involved in the creation and maintenance of a "semantic layer"?
- How does the semantic layer relate to and differ from the physical schema of a data warehouse?
- In generative AI and agentic systems the latest term of art is "context engineering". How does a semantic layer factor into the context management for an agentic analyst?
- What are some of the ways that LLMs/agents can help to populate the semantic layer?
- What are the cases where you want to guard against hallucinations by keeping a human in the loop?
- Beyond a physical semantic layer, what are the other elements of context that you rely on for guiding the activities of your agents?
- What are some utilities that you have found helpful for bootstrapping the structural guidelines for an existing warehouse environment?
- What are the most interesting, innovative, or unexpected ways that you have seen Orion used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Orion?
- When is Orion the wrong choice?
- What do you have planned for the future of Orion?
Contact Info
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
- Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.
- Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
- If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com with your story.
Links
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
