Community Speakers Announced Soon
From Peers in Production
Practitioners from leading data and AI teams show how they put each one to work: The architecture, the tradeoffs, and the lessons that only come from building it and running it.
Context in Practice Summit
Your peers built AI their business trusts.
Now they'll show you how.
- Virtual Summit
- Dec 3
- 11 AM ET
Your catalog holds the context your business runs on. Leading data and AI teams show how they use an Agentic Data Catalog to take it where people ask questions, and into analytics agents the business trusts.
PrukalpaPrukalpa Sankar (Co-founder, Atlan)- Announced soonCommunity speaker
AbhishekAbhishek Mishra (Senior Product Manager, Atlan)
NandiniNandini Tyagi (Strategic Initiatives Lead, Atlan)- Your seat
StevenSteven Hloros (Sr. Customer Education Manager, Atlan)- Announced soonCommunity speaker
HimanshuHimanshu Sikaria (Head of Customer Experience, Atlan)- Announced soonCommunity speaker
Product Playbooks + Production Proof
In just over two hours, see where each part of the Agentic Data Catalog is headed, how your peers put it to work, and what you need to start on your own.
Prukalpa, Abhishek, Nandini, Steven, and Himanshu
From Atlan's Product Leaders
Atlan's product leaders see what's working across every customer building on Atlan. They share the decisions behind each capability, where the industry is headed, and the resources to get you started.
Resources to Take Home
Tools to use right away: Recipes for MCP in any AI tool, skills for trace analysis and AI-ready data reports, and Atlan AI Labs research on structuring context for agents.
- SkillAI-Ready Data Report SkillAbhishek MishraLakehouse
- CookbookMCP CookbookSteven HlorosRollout & Adoption
- SkillTrace Analysis SkillNandini TyagiRollout & Adoption
- ResearchAI Labs ResearchHimanshu SikariaAgents
Agenda
Dec 3 · All times ET
- 11:00AM10 minOpening Keynote
Prukalpa Sankar
Co-founder, Atlan
- 11:10AM25 min
Lakehouse Architecture The Open Foundation for Your Enterprise Context
Data teams want more from their context, like reporting on what's governed and what's ready for AI, straight from the engine they already use. Agents reach for that same context on every call, and catalogs built for people to browse can't keep up. Serving both takes a foundation of its own.
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Product Briefing · 10 minInside the Context Lakehouse
Abhishek MishraSenior Product Manager, AtlanResource · AI-Ready Data Report SkillEverything your catalog knows, including definitions, lineage, policies, and the business knowledge around them, can now live in an open, Iceberg-native store that any engine or agent can query. Abhishek Mishra walks through the decisions behind it and the architecture patterns emerging across the customers who've turned it on: Why it's designed to stay open and portable, how it keeps a record of exactly what context an agent saw on any given day, and how to get started.
Presentation · 15 minWhat It Took to Put a Lakehouse Into ProductionCommunity speakers announced soon
Get a close look at a Context Lakehouse running at enterprise scale, and the blueprint behind it: The design choices the team made, how it fits alongside the rest of their stack, and how it puts governed context within reach of the AI their business is building.
- 11:35AM60 min
Rollout & Adoption What Teams With Real Adoption Have in Common
Well-documented data is a start. The payoff comes when people actually use it: In the chat windows where they ask their questions, and the AI tools and agents working alongside them. Atlan experts and speakers from across the community share what a strong rollout looks like, what brings people back for more, and how every question they ask can make the catalog better.
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Field Notes · 5 minChanging Consumption Patterns in an AI-Native World
Nandini TyagiStrategic Initiatives Lead, AtlanPeople have stopped typing keywords to find data. They ask in plain language, in chat tools like Slack, and more and more from inside the AI tools they already use. Nandini Tyagi has built alongside more than 100 of the world's leading data and AI teams, and walks through what that shift means for adoption: Where people now go for answers, and how to meet them there.
Panel · Conversational Search · 15 minAdoption Lessons From Teams Who've Done ItCommunity speakers announced soon
Users will try a new way to ask about their data once. Whether they come back depends on the answer. With Conversational Search, anyone can ask from Slack, Teams, or their browser and get an answer grounded in governed context. Panelists who've rolled it out trade notes on how to keep a rollout from stalling, and what kept their people asking long after launch day.
Playbook · MCP · 10 minThe Art of the Possible with MCP
Steven HlorosSr. Customer Education Manager, AtlanResource · MCP CookbookMCP can carry your catalog's context into almost any AI tool, but getting it connected is the easy part. The value comes from what you do next: Trace a broken dashboard to its root cause, see what a SQL change will break before you merge, ground AI-written code in your real schema, score assets for AI readiness, or grow your business glossary. Steven Hloros opens up the MCP Cookbook, more than 30 tested recipes gathered from the ways customers put MCP to work, and explains how to contribute your own as it keeps growing.
Panel · MCP · 20 minMCP Use Cases Worth CopyingCommunity speakers announced soon
Many teams already have MCP connecting their context layer to an AI tool or two, but the protocol reaches much further: AI assistants and internal chatbots, engineers' code editors, enterprise search, workflow automation, data pipelines, even agents that keep the catalog documented. Panelists building with it today share the use cases they've put to work, how they decided where MCP fits in their business, and what held up as they built it out.
Playbook · Self-Improving Catalog · 10 minBuilding a Self-Improving Data Catalog
Nandini TyagiStrategic Initiatives Lead, AtlanResource · Trace Analysis SkillCoverage tells you how much of your catalog is documented. It can't tell you whether that context actually worked. Every question people and agents ask leaves a trace, and Nandini Tyagi walks through how teams read those traces to find where answers went wrong, route each gap to the right fix, and make the next answer better than the last.
- 12:35PM40 min
Agents on the Context Layer What It Takes to Build an Agent That Works
Agents get answers wrong when they're missing what a person would know to check, like metrics and semantic models, lineage, access policies, and the business knowledge scattered across documents and people's heads. Practitioners who've shipped analytics agents break down how they built that context together, business domains and end users alongside AI, data engineering, and governance teams, and why it carries over to whatever tool comes next.
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Research · 10 minThe Research Behind Agents That Get It Right
Himanshu SikariaHead of Customer Experience, AtlanResource · AI Labs ResearchAn analytics agent is only as good as the context it's handed, and the hard part is knowing which context matters most. Atlan spent more than a year finding out, building agents alongside customers and putting every piece to the test, from governed metrics to the evals that catch a wrong answer. Himanshu Sikaria shares what held up, so you can plan next year's agents on the context your catalog already holds, without the trial and error it took to get there.
Lightning Talks · 30 minThree Agents in Thirty MinutesCommunity speakers announced soon
Three teams, ten minutes each, one analytics agent apiece: What it does, who it serves, and the context that makes its answers worth trusting.
- 1:15PM5 minClosing Keynote
Prukalpa Sankar
Co-founder, Atlan
Context in Practice Summit
Dec3
Thursday11:00 AM ETVirtual Summit
Practitioners from leading data and AI teams, and Atlan's product leaders.