Genie

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]

Genie Agent content search on a managed volume fails with "The request was invalid"

CUSTOMER STORY | PAR Technology builds modern AI-powered intelligence on Databricks Genie

Announcement | Beyond answers: New Genie One features to turn insights into action

Is it possible to embed a Genie Space inside a Power BI report?

Announcement | Get Started with Genie One: Top AI Cowork Use Cases for Business Users

Operationalizing a Live Genie Space: Benchmarking, Governance & Continuous Improvement

Making Databricks Genie Spaces Actually Work: A Practical Framework for Client and Data Teams

Managing Databricks Genie Agents as Code with Databricks Declarative Automation Bundles (DAB)

Any chance we get Genie One a python sandbox env?

Trying to get a python enviornment on genie one to run for the end-user so they can use the xlxs claude skill. I have found a way to get the skill added into genie one with the scripts, however, as I suspected it does not have a python sandox yet. submitted by /u/BoiBoii619 [link] [comments]

MuleGraph Investigator: Uncovering Money Mule Networks with Databricks Genie

Insurance Intelligence Copilot – Powered by Databricks Genie

SleepLens: Turning Multimodal Sleep Data Into Conversations With Databricks Genie

BI Rationalization Genie: Turning Report Sprawl into Conversational Decisions with Databricks Genie

Crux — Construction Intelligence Powered by Databricks Genie

Databricks Solutions Architect Genie Agent

Building a Databricks Solutions Architect Genie Agent

15+ years in ETL/Data, but relying heavily on AI (Copilot/Genie) lately. Am I still an engineer, or just a prompt validator?

Looking for a reality check from other data folks. I have 15+ years of experience in data (mostly Ab Initio and SQL), but moved to Databricks 2 years ago. I know data architecture and transformation logic well, but my raw Python coding skills are basic. My daily workflow usually looks like this: 1. I figure out the logic or root-cause the pipeline issue. 2. I use Copilot or Databricks Genie to generate the PySpark/Python code. 3. I review, test against edge cases, fix logic flaws, and validate the output. I’m great at step 3—I know how the data should behave. But because I rarely write code line-by-line from scratch anymore, I’ve been hit with huge imposter syndrome. It feels like I’m just a code reviewer for AI rather than a "real" engineer. Has anyone else from a traditional ETL background felt this shift in modern cloud stacks? Is this just the new reality of engineering, or am I letting my skills atrophy? submitted by /u/Terrible_Mud5318 [link] [comments]

Gridiron Genie: an NFL matchup room powered by Databricks Genie Agent

Beyond FIFO with Databricks Genie :Resolving the UK Pensions Dashboards Missing Valuation Bottleneck

Schema management: medallion or domain?

At our company we're considering moving our old stack of a bunch of data integration tools built on top of an Azure MSSQL database to Databricks. I think it will bring a lot of advantages, not in the least cheap storage and no need to use a VM or SQL Studio to manage the data. I've tried out Genie One on the data (connected through federated access), and me and my manager are quite excited about users also getting self-service access in the future. One doubt I keep getting back to myself is how to set up the data access. Using a medallion architecture with three schemas makes access management quite easy: end users get access to gold-tables, the rest not. My concern is with the size: if we migrate our current setup, we'd have more than 100 bronze tables, which doesn't feel like best practice. Our first setup has been to give those tables some kind of prefix name, like bronze.source_table1, (e.g., bronze.hubspot_customerviews, bronze.hubspot_leads). What is generally best practice? Giving user groups access on an individual table basis and storing domain tables together in a schema, or using this three-schema setup? Or some kind of hybrid? Or am I massively overthinking this? submitted by /u/histofafoe [link] [comments]

The Exfiltration: a detective game where the only witness is a Genie agent

Building a Delivery Assurance Agent: Predicting $8.4M in Penalty Risk with Databricks Genie

Multiple Databricks Genie MCP connectors in one Claude workspace collapse into a single shared connector, anyone solved this?

We're connecting Claude (Cowork/Claude Desktop) to several separate Databricks workspaces, each with its own Genie space. Running into a platform-level snag and hoping someone's hit this before. Setup: each workspace exposes the standard Genie MCP endpoint ( https:// /api/2.0/mcp/genie ). We add each as its own connector in Claude so we can ask natural-language questions against the right workspace's data. Problem 1, generic connectors are indistinguishable: every Genie MCP server reports the same tool names and the same generic description, regardless of which workspace it's pointed at. Claude has no way to tell them apart from metadata alone, so it either has to probe each one (asking a neutral "what workspace is this?" question and checking the returned deep_link ) before it can route a question correctly, or it guesses wrong. Problem 2, trying to fix it with named connectors backfires: we tried building a Claude plugin that declares each of the seven as a distinct, fixed-name MCP server (e.g. genie-workspace-a , genie-workspace-b , ...), hoping deterministic names would remove the need for probing entirely. Turns out Claude's client recognizes Databricks Genie as a "verified" connector type in its directory, and collapses all seven fixed-name declarations into a single shared authorization instead of keeping them independent. Authorizing one silently becomes "the" Genie connection, and the others just reflect that same state rather than getting their own. Problem 3, working around the collapse trades one problem for another: we found that changing the declared URL slightly (adding a harmless query param) makes the client stop recognizing it as the verified type, which does force it down the plain custom-connector path and keeps it independent. But then it needs an actual registered OAuth client ID against that Databricks workspace, since it no longer gets whatever implicit OAuth handling the verified integration has. That means registering an app per workspace in Databricks/Azure just to get back to where the plain manual setup already was, more infrastructure, not less. So right now we're back to manually-added generic connectors plus a routing skill that probes and caches per conversation, which works, but costs a lookup the first time each workspace is used per session, and never scales cleanly as we add more workspaces. Has anyone gotten multiple Genie MCP connections into the same Claude account/org without them colliding into one shared authorization? Is there a way to deploy or configure the Genie MCP endpoint itself so each workspace reports distinguishable tool names or descriptions, rather than relying on the client to differentiate them? Btw, in Codex/chatgpt this is not an issue, because we can edit the description/name, and it automatically discovers the right plugin to use. It seems this is a very claude thing issue submitted by /u/Akroma188 [link] [comments]

Le Greffier - A Genie Agent Powered Game

Weird behavior of etag parameter in Genie Space while using Databricks Python SDK and REST API

Hi everyone, I’m developing a FastAPI based application that interacts with Databricks to automate QA testing. As part of this, I’m trying to create and modify Genie Spaces so that I can scope a space to different tables and configure its behavior. The main thing I’m interested in modifying is `serialized_space`, which contains the Genie configuration such as scoped tables, instructions, SQL examples, etc. I’m using the etag returned by the `GET` API for optimistic concurrency, so that I don’t accidentally overwrite someone else’s changes. The flow is basically: Get the Genie Space and its `etag` + `serialized_space`. Make a small change to the JSON configuration(serialized_space). Send the complete `serialized_space` back along with the etag. Verify the result. I’m seeing some strange behaviour specifically with changes to the table list. etag updates that work: No operation update of the fetched configuration Updating text instructions Adding/updating SQL examples Updating nested fields inside a table/column configuration Removing a column from an existing table(Doesn't seem to reflect in the databricks UI) Not an issue for now etag updates that fail: Adding a table to `data_sources.tables` Removing a table from `data_sources.tables` In both cases, I get: Aborted: Space configuration has been modified since this export was taken. Re-export the space and merge your changes, or omit the etag to skip conflict detection. What makes this confusing is that I’m fetching the etag immediately before the update, and the exact same configuration update works if I simply omit the etag. I also tested this using both the Databricks Python SDK: w.genie.update_space(...) and a raw `PATCH` request through the SDK HTTP client. Both behave the same way. I also verified that when the etag protected request is rejected, the Genie Space remains unchanged in the UI. Has anyone else seen this behaviour? Is etag-based optimistic concurrency intentionally not supported when changing the membership of `data_sources.tables` (adding/removing tables)? If I omit the etag and perform the same table addition/removal operation, the update succeeds. Any insight into how Genie handles etags for `serialized_space` changes would be really helpful. Thanks! TLDR : ETag-protected updates work for instructions, SQL examples, and nested column changes, but fail when adding/removing tables from data_sources.tables, even when using the latest ETag. The exact same table changes succeed when the ETag is omitted. Has anyone seen this, and is this expected Genie behavior? Have used LLM to format the post !!! submitted by /u/Firefly_KM [link] [comments]

Genie One Mobile for Genie Agents, Dashboards, and Apps

Finally got to try the Genie One mobile app. In the first GIF, you see me asking a question, followed by a follow-up. In the second one, I ask Genie to schedule this type of analysis for me and email it to me every week. Time was sped up for illustration purposes, depending on the complexity of the question, the answer takes seconds to minutes. Initial prompt in particular took longer, but follow ups returned quickly. Please note in the demo I was using unrefined dummy data that is not current (hence why you see 2024 as the year when I ask for "last year"). I did also try Dashboards on Genie One. It was good, though I would say the delivery on that end felt a bit more basic than it should be. Didn't get to try Apps, which it also supports. At the end of the day, if you like working with Genie Agents (formerly Genie Spaces), you are likely to really like it on your phone. You could be in a meeting and as soon as a good question pops up, ask the question from your data on Genie, and before the meeting ends, you likely have an answer. Hope you found this helpful! submitted by /u/JosueBogran [link] [comments]

What is the general guidance for data structure when using a text to SQL agent such as Databricks Genie?

I’ve heard use of metric views is highly recommended, but never sure if OBT approach or normalized approach works better. If I’m defining the joins in my Genie, shouldn’t it be able to pull things either way? submitted by /u/ceeej777 [link] [comments]

Azure Databricks Genie + Copilot Studio: MCP tools discovered and enabled, but Copilot never invokes

You can now put the Databricks account console, account-level Genie One, and Custom URLs behind Private Link

You can now put the Databricks account console, account-level Genie One, and Custom URLs behind Private Link, so your whole account is reachable without touching the public internet. If a security review ever stalled a Genie One or account console rollout because those surfaces were only reachable over the public internet, that objection is now removable. And if you have been standing up one private endpoint per workspace and per region, you can stop. Read more: https://www.linkedin.com/posts/cenh_databricks-dataengineering-networksecurity-ugcPost-7497673121861369856-6IiF/?utm_source=share&utm_medium=member_desktop&rcm=ACoAABmJHrsBNAC3x3H1M58JRKoHv_l4D61n0-8 submitted by /u/Lenkz [link] [comments]

Genie Agents dude architecture

submitted by /u/Chrisesc0 [link] [comments]

Genie Agents dude architecture

Hi, I have a question now that I’m planning to start a Genie Agents project and am organizing my architecture. I’m working from SAP BDC, and I plan to create two workspaces in Databricks—one for DEV and one for PRD. But I’m not sure where it makes the most sense to create the Genie Agent. Technically, the queries and tests would be better suited for the PRD workspace; running them in DEV doesn’t make much sense because the Genie Agent DEV is connected to DEV tables. But then, what role does CI/CD play here for Genie Agents? Or am I misunderstanding the architecture, and should I create two Genie Agents (DEV and PRD) in the PRD workspace? Thank you very much if you can help me with this question. submitted by /u/Chrisesc0 [link] [comments]

Recording | BrickTalk: Mastering Databricks Genie Capabilities

Announcement | How Databricks Genie Code Automated 90% of Data Ingestion for a Major Railroad