Databricks is the Data and AI company, helping organizations build and scale data and AI apps, analytics and agents.
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Genie One just got a major set of updates across context, data access, collaboration, and automation. 🧞
Here's what you can do now:
• Ask questions with business-aware context built in. Genie Ontology is enabled by default
• Apply your org's data conventions to every prompt with workspace instructions
• Upload Word docs, images, CSVs, spreadsheets, and PDFs directly into your workflow
• Query Unity Catalog tables inside Genie with schema preview and one-click access requests
• Automate recurring work with scheduled tasks that reference past runs for consistent outputs
@YoussefMrini walks through all of it in under 3 minutes:
piped.video/watch?v=hRayT1rQ…
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Ever tried getting Claude Code and Codex to actually work together without playing copy-paste between terminals?
@leonvz takes Omnigent, our new open-source meta-harness, for a spin to show how multiple coding agents can share sessions, rules and security policies in one system.
He demos forking work across agents, multi-agent review and debate with Debby, and splitting implementation across subagents with Polly.
Watch the full walkthrough:
piped.video/watch?v=gYtQ1LKS…
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What changes when more of your Postgres working set stays in DRAM instead of falling through to slower cache and storage layers?
For fixed-size Lakebase Postgres computes with CU >= 80, we now set shared buffers to 75% of DRAM and disable the local file cache, keeping hot pages in the lowest-latency access path.
In production, large endpoints have seen up to 2x throughput, fewer reads from the storage layer and lower latency after the change.
See how we’re improving Lakebase Postgres compute caching:
databricks.com/blog/improvin…
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Databricks retweeted
Everyone has been obsessed by Jev and what it can do, and they should be! Fast typed decisions >> slow text generation. It's a complete game changer. But how do you do that with all the data you have?
We just dropped 𝚊𝚒_𝚍𝚎𝚌𝚒𝚍𝚎(), which runs decision models natively on all your data in @databricks . Now you can run system one decisions on all your big data, see screenshot.
databricks.com/blog/introduc…
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Databricks retweeted
Not every marketer is familiar with Databricks today, but that's about to change!
Marketing agents simply do not work without real-time customer+business context. Databricks' CustomerLake is quickly becoming the cornerstone of the marketing stack of the future, providing just that.
It was great discussing all of that on stage at Braze's Forge conference. Thank you for having me, @jon_hyman!
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Coding tasks aren’t equally difficult, so why send every one to the same model?
Smart Routing evaluates each task separately and selects the lowest-cost model capable of doing the job, helping balance model quality, latency and cost.
In this demo, Omnigent splits an app build across planning, backend and frontend work, routes each task to a different model, and runs some of them in parallel.
Learn more: bit.ly/smart-routingai
GitHub repo for this series: github.com/viktoriasemaan/ag…
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Databricks retweeted
🎉 Introducing Databricks AI Decide: make fast decisions on your governed data
Following the TypeSafe AI Jev launch, we’ve seen increasing demand for a fast, low-cost API that turns raw text into structured decisions. However, many of our enterprise customers are unable to use Jev due to data privacy and access concerns.
AI Decide is our enterprise-grade function for fast decisions on customer’s governed data. It is supported for batch use cases like processing millions of documents with SQL and for realtime applications like model routing through our REST API. Starting early next week, customers will also be able to govern permissions on the ai_decide function through Databricks Unity Gateway.
A big shoutout to @mattydtweetz @ivanzhouyq @nihit_desai @hanlintang for getting this new function launched in less than a week!
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How much of your AI workload is really just making a decision?
ai_decide is a new Databricks AI Function built for fast, structured decisions over governed data. It classifies, scores and chooses what happens next in a fraction of a second, with lower latency and cost than an LLM on similar tasks.
Use it for model routing, document processing, agent evaluations and real-time app logic 🐍 databricks.com/blog/introduc…
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Agentic apps are putting new pressure on the data stack.
Join Databricks Co-founder and Chief Architect @rxin to explore why Postgres is the foundation for a new data processing architecture, and how LTAP lets transactions and analytics read from the same copy of data.
Learn how to:
- Bring agent memory, application state and historical data under the same permissions
- Remove sync intervals as a bottleneck in the agent memory loop
- Maintain compatibility with existing Postgres drivers, extensions and ORMs
You’ll also see the architecture in action through demos and enterprise patterns.
Live November 10–12. Register now: databricks.com/resources/web…
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Databricks retweeted
Excited to announce our new partnership with @DecagonAI to bring richer enterprise data intelligence directly into customer conversations.
AI agents are only as useful as the context they can access.
Enterprises already have an incredibly rich record of their customers in Databricks. Orders, payments, product usage, preferences, and countless other signals all help explain who a customer is and what they need.
Through this partnership, Decagon agents can access governed data from the Databricks when they need it, while insights from those conversations can flow back into Databricks and become useful across the rest of the business.
We’re excited to work with the Decagon team to make enterprise customer agents more context aware while keeping data governance at the center.
decagon.ai/blog/decagon-and-…
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Databricks retweeted
We’re partnering with @Databricks to bring enterprise data intelligence into every Decagon customer conversation.
Agents can access governed customer data directly from a company’s lakehouse, while conversation insights flow back into Databricks.
This gives agents richer context in every conversation and teams a more complete view of their customers.
Decagon is also a launch partner for Databricks’ new Transactable Marketplace and will be available through Databricks Marketplace.
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Databricks retweeted
Omnigent v0.16.0 is now available! 🎉
This release updates desktop onboarding, the workspace browser, sandbox controls, and agent configuration.
🧭 Redesigned desktop onboarding
🗂️ Unified workspace browser
📦 Sandbox copy-on-write and clones
🔐 Admin sharing and Slack defaults
🧩 Pi prompt controls, plus native images for supported harnesses
Full release notes 👉 omnigent.ai/releases/0.16.0
#Omnigent #AIAgents #OpenSource
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Two new frontier models just landed on Databricks.
@OpenAI GPT-6.1 Sol and @SpaceXAI Grok 4.7 are both live today.
GPT-6.1 Sol leads the cost-quality Pareto frontier on OfficeQA Pro v2. Grok 4.7 reaches the frontier at enterprise document parsing. Two benchmark leaders, available the day they ship.
Frontier models are shipping faster than ever. Unity Gateway keeps you right there with them. It picks the right model for the right task, governs every call, and controls costs across GPT-6.1 Sol, Grok 4.7, and 60+ other frontier and open models already on Databricks.
Try GPT-6.1 Sol and Grok 4.7 today: docs.databricks.com/aws/en/r…
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AI doesn’t have an intelligence problem. It has a context problem.
When business context is scattered across dashboards, documents, tickets and chats, users struggle to get fast, trusted answers and data teams stay stuck fielding ad hoc requests.
See what it takes to close that gap with a unified context layer that helps AI deliver trusted answers, act autonomously and give teams back time. Go under the hood of Genie Ontology and see the approach in action with a product demo.
Watch on-demand: databricks.com/resources/web…
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Databricks retweeted
We've moved to an approach @databricks that gives "Day 1" access to 12,000 employees whenever a new frontier model is released. Very tricky to do while avoiding massive cost spikes and quality regressions. We've written up our approach if others are interested (see below).
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At Databricks, we want employees using the best models on Day 1. That means moving quickly when models like Opus 5.5 and GPT-6 Sol launch, while evaluating real-world usage and managing the cost impact across thousands of employees.
Our AI engineering team uses Unity Gateway to manage access, spend and model selection at scale, then determines which models belong in our AI stack.
Learn how we roll out frontier models across Databricks: databricks.com/blog/how-data…
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.@AnthropicAI's Claude Sonnet 5.5 is now available on Databricks across AWS, Azure and GCP, governed by Unity Gateway.
It improves efficiency over Sonnet 5 for coding and agentic use cases, and reached Opus 5-level accuracy on document understanding, parsing and search. It joins Claude Opus 5.5, Claude Fable 5.1 and 60+ open-source and frontier models on Databricks.
Build domain-specific agents with Agent Bricks, deploy them as Databricks Apps with Lakebase-powered memory, and govern every call through Unity Gateway.
See documentation: docs.databricks.com/aws/en/r…
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AI agents need fast, accurate search at massive scale. Lakebase Search is now GA, bringing scalable vector and BM25 full-text search directly into Postgres.
- New frontier for price-performance, latency, and recall on VectorDBBench
- 4x cheaper than running pgvector for the same workload
- True pay-per-use with zero compute cost when idle
databricks.com/blog/lakebase…
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Databricks retweeted
We just tested the latest AI models on 2,400 engineers. The cost/quality frontier just shifted massively.
The TL;DR:
• Opus 5.5: Our new daily default - higher quality and 20% cheaper than prior baselines.
• GPT-6 Luna: Shockingly capable and 20x cheaper than Opus 5.5 for high-volume routing.
Must-read for anyone managing production AI workloads 👇
Crazy few weeks for model releases! Our findings @Databricks show several new models meaningfully advance the pareto frontier. Results below (online workload analysis of N=2,400 engineers, plus offline evals):
1. Two of the three models released last week clearly expand the cost/quality frontier: Opus 5.5 and GPT-6 Luna.
2. Opus 5.5 is now the highest quality mid-tier model. It is better than all prior Opus models, better than GPT-6 Sol, and better than GPT-5.6 Sol.
3. Opus 5.5 reduces same-task costs consistently by 20% in both offline and online analysis. This is against a baseline of Opus 4.8, the prior least-cost Opus model (Opus 5.0 was a bit of a dud with high costs and barely noticeable quality improvements).
4. Due to best-in-class quality and lower costs, Opus 5.5 is a strong candidate as an “every day default” model for coding, and we are now encouraging it for this purpose at Databricks.
5. GPT-6 Luna is very, very, very cheap. It was at least 20 times cheaper per-task than Opus 5.5 in every offline benchmark we tested and in observed online use.
6. GPT-6 Luna is surprisingly capable given how cheap it is. On one of our most difficult evaluation suites it roughly matches Opus 4.6 performance, while being 99.3% cheaper per-task than Opus 4.6 was at that time. That's a 100X cost reduction in ~9 months! This finding is preliminary and we are still evaluating Luna quality on a broader set of offline and online tests.
Our production setup: Unity Gateway to route workloads across models and trace agentic interactions. A mix of end-user harnesses including: Omingent (meta-harness), Claude Code, Codex, and Cursor.
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Databricks retweeted
Crazy few weeks for model releases! Our findings @Databricks show several new models meaningfully advance the pareto frontier. Results below (online workload analysis of N=2,400 engineers, plus offline evals):
1. Two of the three models released last week clearly expand the cost/quality frontier: Opus 5.5 and GPT-6 Luna.
2. Opus 5.5 is now the highest quality mid-tier model. It is better than all prior Opus models, better than GPT-6 Sol, and better than GPT-5.6 Sol.
3. Opus 5.5 reduces same-task costs consistently by 20% in both offline and online analysis. This is against a baseline of Opus 4.8, the prior least-cost Opus model (Opus 5.0 was a bit of a dud with high costs and barely noticeable quality improvements).
4. Due to best-in-class quality and lower costs, Opus 5.5 is a strong candidate as an “every day default” model for coding, and we are now encouraging it for this purpose at Databricks.
5. GPT-6 Luna is very, very, very cheap. It was at least 20 times cheaper per-task than Opus 5.5 in every offline benchmark we tested and in observed online use.
6. GPT-6 Luna is surprisingly capable given how cheap it is. On one of our most difficult evaluation suites it roughly matches Opus 4.6 performance, while being 99.3% cheaper per-task than Opus 4.6 was at that time. That's a 100X cost reduction in ~9 months! This finding is preliminary and we are still evaluating Luna quality on a broader set of offline and online tests.
Our production setup: Unity Gateway to route workloads across models and trace agentic interactions. A mix of end-user harnesses including: Omingent (meta-harness), Claude Code, Codex, and Cursor.
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