Genie

I am trying to build an interactive dashboard on the underlying Databricks. Which of these are the best?

I’m thinking of 4 options here 1. Build an MCP (a custom MCP) that can access custom tools on Databricks and interface it on Claude.ai or Claude desktop Advantage- Claude is very good at inferencing, multi-turn conversation and multi-step processing Disadvantage - custom MCP and tools needs to be built accurately and validated. It should have full context of schema and unity catalog Build a semi-custom MCP - this will use “askGenie “ as one of its tools with additional custom tools Advantage- complexity decreases as we leverage genie space Disadvantage- double inference by genie and Claude Use custom Databricks connector in Claude. Not sure if this uses genie and therefore double inference but it’s more reliable than custom build because this is a native offering by vendor Use only genie and build custom dashboard without needing Claude interface What’s the thought on this? submitted by /u/Bala_Devaraj [link] [comments]

Announcement | Five ways marketers can use Genie One

Announcement | The Genie One MCP is now Generally Available

Lessons learned from configuring Genie Agents

Time to Swap the Cookies for Jetfuel - New Dataset & New Databricks Genie Tutorial

Most of you know samples.bakehouse . Great for a first query. Perfect for a quick demo. But after years of cookie sales, it's a little overbaked. Time to swap the cookies for jet fuel. ✈️ Together with the OpenSky Network , I brought a full day of global air traffic to Databricks Marketplace: 696 million real ADS-B position reports, messy just like real life. Myself, I used Genie for the whole journey: EDA, data exploration, a Apache Spark Declarative Pipeline, and a Lakeflow Job. Then I went a step further and read the same Marketplace data with open-source tools only, using OpenSharing and pandas. The result is this hands-on tutorial: Marketplace + Unity Catalog: get the data as a governed table Genie Agents: find anomalies in plain English Genie Agents: explore and visualize with maps and charts Genie Code: a Spark Declarative Pipeline, bronze to gold, with data quality rules Genie Code: a Lakeflow Job with schedule, retries, and email alerts Databricks Apps: your coding agent, governed by Unity Catalog OpenSharing: the open-source client in VS Code with pandas Everything runs on Databricks Free Edition (free, no credit card). 📖 Tutorial: Databricks Genie for Data Engineers and Data Scientists 💻 GitHub: databricks/tmm/DSDE-Genie-Tutorial 🛫 Dataset: OpenSky Network full-day dataset on Marketplace What's the first thing you'd query in a day of global air traffic? P.S. For the record, we still love bakehouse! 🍪❤️ [Disclaimer: I'm one of the two people who baked it.] submitted by /u/CompetitiveBet8978 [link] [comments]

Claude Academy's Using Databricks for Data Analysis - This official documentation from Anthropic on Claude Academy for how to work with Databricks is really underwhelming, so I wanted to share a guide that properly explains everything.

Hey Databricks community! I have been working with Databricks as an admin for over 4 years and I was attempting to follow this official tutorial from Anthropic for how to connect Claude with Databricks https://academy.claude.com/tutorials/using-databricks-for-data-analysis And I encountered multiple significant frustrations with doing it, as the tutorial is very underexplained and outdated for how to get Claude working with Genie One and other functions within Databricks. Therefore, I thought this video on Youtube would be valuable content to share to help solve this problem, for other people that are encountering similar difficulties when getting these two tools connected and building out systems that work for Claude to properly interact with Databricks. Let me know if this video is helpful! submitted by /u/k_kool_ruler [link] [comments]

Unlock the Power: Databricks Genie One vs. Code and Agent

Genie One Foundations: What Data Teams Should Get Right Before Rolling It Out

Announcement | Genie One MCP: Give any AI Agent the Right Business Context

Genie agent hyperlink creation

Genie Agents citation (bug)

Ontology ranked the snippet. I wrote the gate I wanted before trusting a Genie space.

A commercial user asked what yield was last season. Two definitions live in the same lakehouse. One is plot-level, moisture-adjusted, borders out. The other is as-harvested, and a dashboard still uses it. Genie returned the second number. Not a hallucination. OntoRank did what it does: creator, usage, link to a certified asset, freshness. The loud dashboard won. Nobody had certified the measure the board pack is allowed to quote. That is the failure I care about. A green demo is not a release gate. What I actually require before that space faces a user who will paste the sentence into Slack: Golden questions written before the space exists. If you write them after you have tuned instructions, you are scoring an overfit prompt. Benchmarks are not instructions. Genie does not learn from the benchmark SQL. The number lives in a Metric View. A column comment and a knowledge-store formula are hints. Ontology will rank all three. Only the Metric View is a contract every surface compiles. Same KPI from MEASURE(), from the certified dashboard, and from Genie. If they disagree, the space stays off Slack. Permissions checked as the asker. Inside a Genie Agent, compute is the author’s warehouse and data access is the end user. That split does not automatically hold in Teams, Copilot on maker credentials, or a Databricks App on its default service principal. The row filter still runs. It runs for whoever showed up on the SQL. A written kill line. I use Databricks’ “above 80% before UAT” as the hold line, not the ship line. Ship is 90% and zero fails on a regulated definition. Any miss on that definition takes the space down. A person does that. Not a service principal. Genie Code can draft the Metric View and the MERGE. ZeroOps can propose a fix and wait for Approve. Neither owns the definition. If the job stays green for eleven days because a key was slightly wrong, that is still your incident. I wrote the four pieces up so I would stop re-explaining this in reviews. Argue the 80 vs 90 line if you want. I will not move a regulated KPI to “looks fine in the demo.” submitted by /u/Worth_Phase1358 [link] [comments]

Deploy Genie Agents and Dashboards to multiple environments

Hey, I had to struggle a bit with deploying dashboards and Genie agents while setting UC paths dynamically. The possibilities offered by native bundle config didn't feel quite right. In some cases "dataset_catalog" and "dataset_schema" are enough. However this doesn't work with UC-managed metric views in dashboards and Genie agents don't even support these parameters. Using serialized dashboard and agents instead of files was one solution but the development flow of making changes in the UI, transferring them into the serialized parameter and then replacing values with variables again didn't feel smooth. So I started this CLI to update the JSON files in a more structured and convenient way. Since everyone is currently hyped about Ontology I also added a linter for Genie agents based on the recommended best practices from the Ontology and Genie Workbench repositories. Happy to get some feedback and hear about any gaps you're currently facing that could be addressed with some utility glue to add some more features. https://github.com/BenSchr/lamp-ops submitted by /u/SwimmingVegetable135 [link] [comments]

Genie Agent SDK visualization

Genie One MCP is now GA

The Genie One MCP server is now GA. It exposes Genie One over MCP, allowing any agent to communicate with Genie One as a peer agent. The MCP exposes tools for asking questions to Genie One, getting query results, checking on incremental progress, and steering responses. These capabilities allow you to integrate Genie One into whatever agent you want without changing your workflow. And you get all of the semantics built-in instead of having to use raw SQL APIs with fragmented skills/stale markdown repos/additional semantic layers like we used to with ai devtools. Here are the docs: https://docs.databricks.com/aws/en/agents/mcp/genie-mcp I've seen some pretty cool use cases with integration into ChatGPT/Claude, but also headless agent workflows where you want to delegate the data questions to Genie One so it can use agents/ontology. Interested in folks' thoughts on the right/wrong use cases for this feature. submitted by /u/lakehouse_vacation [link] [comments]

CUSTOMER STORY | Databricks Genie retires the dashboard backlog at Grupo Panvel

How I Would Evaluate a Genie Space Before I Trust It

Two numbers, same season, same lakehouse. Both called “yield.” One was plot-level, moisture-adjusted, borders excluded. The other was field-level, as-harvested, borders in. A commercial user asked Genie what yield was in India last season and got the second number. Not a hallucination — a definition nobody certified, ranked in because a dashboard used it last quarter. That’s the failure mode I care about. Ontology ranks the snippet. Eval decides whether you ship. Six tests. Not a vibe check. Golden questions — write 10–20 before you build the space Dashboard = Genie — same metric, same number, or it fails ACL leak — trial/internal definitions must not reach commercial users Citation — if it can’t point at a certified object, don’t ship it Drift — change one thing, re-run the whole suite Kill switch — hold, ship, or kill. “The demo worked” is not a gate If you can’t say which definition Genie will pick for yield / revenue / active users, the space isn’t ready. Full write-up: https://medium.com/ai-that-ships/how-i-would-evaluate-a-genie-space-before-i-trust-it-ca07abade9eb What’s the metric in your org with two official-looking definitions? submitted by /u/Worth_Phase1358 [link] [comments]

Genie Agent refuses to add scalar SQL function from Unity Catalog

Tutorial: Databricks Genie for Data Engineers and Data Scientists

Beyond the Dashboard: How Transferz Built a Truly Data-Driven Company Culture With AI/BI Genie

Could a “Data → Agent” composer be useful for Databricks?

I've been thinking about a gap between Databricks data and agent frameworks. Databricks already has a lot of the building blocks: - Unity Catalog - Genie / Genie Agents - MCP - Vector Search - AI Gateway - Agent skills/tools - MLflow - Omnigent / Kasal And tools like Omnigent and Kasal already solve a lot of the agent orchestration/execution side. But I'm wondering about the step before that: What if a customer already has a large, curated and governed Databricks data estate — how do we turn that data estate into an agent-ready configuration without manually wiring everything together? Something like: Existing Databricks Data Estate ↓ Data-to-Agent Composer ↓ ┌────────┼─────────┐ ↓ ↓ ↓ Domains Semantics Metrics ↓ ↓ ↓ Genie MCP Skills └────────┼─────────┘ ↓ Agent Configuration ↓ Omnigent / Kasal ↓ Agent The idea wouldn't be to build another chatbot or another agent framework. It would be a Databricks-native composition/bootstrapping layer that understands an existing Unity Catalog/data estate and generates the pieces needed for agents to work with that data — domain boundaries, semantic context, approved tools, Genie configuration, MCP exposure, skills, policies, evaluation setup, etc. In other words: Kasal/Omnigent: Agent → Tools/Data Proposed layer: Data Estate → Agent I'm curious if this is already solved somewhere in the Databricks ecosystem, or if people are currently doing this manually when building enterprise data agents. Would love to hear how others are approaching the “existing data estate → production-ready data agent” problem. submitted by /u/imsuryya [link] [comments]

Azure AI Foundry + Databricks Architecture | Deploy Genie Agent with DAB...

Azure AI Foundry Databricks architecture, Deploy Genie Agent with DABs, Databricks Genie Agent, Azure Databricks Genie Space, how to deploy genie agent with declarative automation bundles, azure ai foundry + databricks integration, fully operating genie architecture databricks, databricks unity catalog genie agent, azure databricks bronze silver gold architecture, agent to agent nlq databricks, databricks spark python sql delta lake unity catalog, production ready genie agent deployment, databricks vector search index genie, microsoft purview databricks governance submitted by /u/macxima [link] [comments]

Building Deterministic Databricks Genie Agents

Genie Agent's idea of "the Midwest" includes Kentucky. Ours doesn't. Notes from [8 months] of Genie Spaces in prod.

Genie Space has been live for our sales folks for 8 months, maybe 500+ regular users. Short version of what I've learned, since everything I read before setting one up was either a demo or an argument about whether analysts are getting replaced. The failure mode isn't an error. It's a number that's slightly wrong and totally believable. Someone asked how the Midwest was doing, the number looked fine, sat in a deck for weeks. Genie's Midwest includes Kentucky. Our territory map doesn't. You can't catch that by looking at the output - you catch it when finance does. Four things you're configuring, roughly in order of how much they've mattered: Column comments. Free, and the biggest lever by far. Genie reads COMMENT metadata before writing SQL. No comment and segment is just a word - it has no idea whether your values are Enterprise/Mid-Market/SMB or something else, so it guesses. COMMENT ON COLUMN vw_sales_summary.segment IS 'Customer tier: Enterprise (>$1M ARR), Mid-Market ($100K-$1M ARR), SMB ( One pre-joined view, not raw tables. I did raw tables first. Every join it has to figure out is a coin flip. Also, put your test-data filter in the view - then every question anyone ever asks inherits it and you're not trusting the model to remember. SQL expressions. Register a named metric with your SQL and it uses yours instead of inventing one. Ask ten people what an "active customer" is and you'll get eleven answers; this is the box where you settle it. Name them how people talk - "Active Customers" matches, cnt_dist_cust_qtd never will. Example Q&A pairs. Nothing gets retrained, they just sit in context when something similar comes in. The shape travels further than I expected — registered revenue-by-category with a cancelled-order exclusion, and a Q2 question a month later inherited the exclusion in a query I never wrote. Two things from the instructions box worth stealing. One, tell it to ask instead of guessing when the time period is unclear - people trust it more when it occasionally asks. Two, ours has a rule about test customers with a TST_ prefix, whose orders carry real statuses so the status filter misses them entirely. Everyone on the team knew that. Nobody had ever written it down. Curious what other people have ended up putting in their instructions box. Assume everybody hits their own Kentucky eventually. (Here is the longer version with more SQL is on SQLServerCentral, it's mine https://www.sqlservercentral.com/articles/databricks-genie-spaces-for-sql-analysts-natural-language-querying-without-leaving-your-data-platform but the above is the useful part) submitted by /u/mehulbhuva [link] [comments]

CUSTOMER STORY | Rippling powers AI-driven GTM with Genie Agents on Databricks

What's new in Genie One - August 2026

submitted by /u/Youssef_Mrini [link] [comments]

Expanding Genie Agents: Deep analysis, file reasoning, and more

Genie Agent response Export to PDF via API

CUSTOMER STORY | Scottish Water: Capital Investment Insights via Databricks Genie

What's new in Databricks Genie Agents in August 2026 ?

submitted by /u/Youssef_Mrini [link] [comments]

What’s new in Databricks - August 2026

Databricks shipped many major Generally Available features in August 2026. Here is the breakdown of what just landed: 🚀 Unity AI Gateway Enterprise AI governance layer covering model access, Model Context Protocol (MCP) management, and cost observability. 🔒 Role-Based Access Control (RBAC) Switch to scoped, temporary role assumptions instead of dealing with permission bloat. 🔑 Secrets in Unity Catalog Unified security secrets are now governed, 3-level namespace securable objects. ⚙️ Serverless Compute Access Control Granular admin controls over who can trigger serverless workloads across your organization. ⚡ Lakebase Postgres APIs & LTAP Direct Writes Accelerated synced-table loads and improved transactional data integration. 🤖 Genie Agent Upgrades Official GA releases for both the Agent mode API and Full-page Genie Code view. submitted by /u/Youssef_Mrini [link] [comments]

Genie Agents Toggle between Agent and Chat Modes

Do you guys have this issue where in genie agents, after you ask your first question, the toggle between Agent mode and Chat mode just disappears? And you have to exit that chat to get it back? Before: https://preview.redd.it/cl6wec5nuknh1.png?width=739&format=png&auto=webp&s=d2a0bb2c78b63a962ecdf28aa17dc6b47d806f04 After: https://preview.redd.it/bhsleq3ouknh1.png?width=733&format=png&auto=webp&s=ce877c577b07410124b3bd6f97648dfcb46eca43 Idk if this is because I'm on the free edition/free trial of Databricks. However, I'm confused because in the second video demo in this link, the toggle still exists after sending a question to the AI: https://docs.databricks.com/aws/en/genie-agents/concepts submitted by /u/kcxl [link] [comments]