Rishabh Singh

Research Lead @Databricks. Previously @Meta GenAI, Google Brain @GoogleAI, @MSFTResearch, @MIT_CSAIL @IITKgp

[Rishabh Singh @rishabhs]

Super excited to share some of our research on making Genie the best data agent. Data Agents open up a new research frontier for solving complex, real-world enterprise challenges. I was recently discussing with a colleague whether there are still interesting research challenges for Coding agents, and while I believe there are still many challenges there (a topic for another day!), I wanted to highlight some of the unique research challenges for Data Agents and how we tackle them to get up to 3x accuracy improvements on top of coding agents!
https://t.co/SbwG5dYYNt

[Rishabh Singh @rishabhs]

Enterprise context is key to making knowledge agents perform well in the real world.

Existing data and knowledge agent benchmarks primarily focus on tabular data, whereas actual enterprise tasks often require leveraging business context present in multiple sources such as dashboards, notebooks, historical queries, documents, and metric views.

We partnered with @turingcom to create a new benchmark that reflects this real-world enterprise complexity. We observed that Genie (powered by Genie Ontology) significantly outperforms frontier agents:
• 1.6x accuracy
• 4x faster
• 50% fewer tokens

More details: https://t.co/uOCoIFF1Hl

[Matei Zaharia @matei_zaharia]

Genie has transformed how I work as CTO at Databricks, even more than coding agents. It connects across production data, GitHub, Drive, Slack and the web, and automatically learns from this data (Genie Ontology), so I can ask it to do stuff like:

- Tell me how competitors position vs each of our products and which critiques are valid
- Find when and why a design decision was made
- Find bugs based on production events + code changes
- Prototype code changes that would speed up our slowest services in production

Really, it can do so much stuff that I would never even bother to ask a person that it totally changes how you work and the size of tasks you can take on directly. Learn more about our latest release of Genie: https://t.co/nACgmCweKq

[Ali Ghodsi @alighodsi]

A year ago, I ran @databricks totally differently from today. Getting stuff done at Databricks involved calling ppl, scheduling meetings, and lots of back and forth.

Today, I just wake up and use Genie, which works for me in the background. I often just use the phone app to queue up work in the background.

Three examples from just this week:
→ Org charts. How do we flatten the org? It literally found me the hotspots where many projects had gotten stuck. Turns out the data was already in the ontology graph.

→ New internal project everyone raves about (Keanu). Genie monitored the slack channel, got all the insights, correlated them with the source code, and gave me a dashboard of what I needed.

→ New culture survey. Used ontology to figure out from the org chart, work output, and survey scores, which leaders are AI pilled. Why certain scores (e.g. work-life balance) are so low.

These are things that used to take me hours and many meetings, often waiting for the data science team. This is possible because Genie has access to an Agentic Ontology, so it proactively does all the work in the background for me.

Learn more about our next-generation release of Genie:
https://t.co/H5nRGviy15

[Databricks @databricks]

𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝘁𝗵𝗲 𝗻𝗲𝘄 𝗚𝗲𝗻𝗶𝗲: an always-on, autonomous AI coworker that proactively handles long-running work within your organization's guardrails.

AI assistants can answer questions and run analyses. But most enterprise work is complex and continuous, requiring teams to track changes, follow up, and take action.

Genie Ontology is now generally available, providing the shared business context behind it. The agentic ontology now includes new knowledge sources and new ways to curate and govern that context.

New capabilities across Genie Agents and plugins extend that same context to more workflows and AI tools.

Here's what's new: https://t.co/Cl3CJmYgOV

[Ali Ghodsi @alighodsi]

@RamaswmySridhar

[Pushmeet Kohli @pushmeet]

Announcing a major milestone achieved by my team @GoogleDeepMind: we have proven key security properties of the machine code underlying the Signal messaging protocol. This is the first time the actual production code of a widely deployed protocol has been formally verified – by any method, with or without AI – and at this depth.

More than a research breakthrough, it’s a prime example of how frontier AI can be a force multiplier to counter the speed and scale of modern cyber threats.

Formal verification offers a higher standard of security; however, it’s extremely complex and time consuming, which is why in many cases it’s considered to be impractical. Similar efforts could take teams decades of combined effort. We did it in a month’s time.

Using AlphaProof Code, Google DeepMind's new agentic framework for formal verification that runs on LLMs including Gemini Carbon, we verified the Signal protocol down to the actual code running on devices.  This milestone lays the groundwork for a future in which vital systems, from medical device firmware to power grid software, ship with mathematical proof that their protections work.

Reaching that future will take continued research from our community. In that spirit, we're open-sourcing our formal specifications and verification artifacts so others can build on this work.

On a personal note, this moment is of deep significance to me. I started my own research journey more than two decades ago as an intern working on checking the consistency of complex software systems. Seeing this grand challenge made practical with modern AI is extraordinary.

I'm extremely proud of my team members: Swarat Chaudhuri, Andrew Ferraiuolo, Anton Kovsharov, Tej Chajed, Sam Schlesinger,  Karn Seth, Jonathan Katz, and Charles Sutton for taking on this grand challenge and delivering, and grateful to the Signal team for their partnership.

https://t.co/kN1eRYQ8fF

[Swarat Chaudhuri @swarat]

Thrilled to announce that our @GoogleDeepMind team has cracked a major challenge in software engineering and cybersecurity: formally verifying a real-world implementation of the #Signal cryptographic protocol at the machine-code level. This proof was done using AlphaProof Code, our new AI framework for formal specification and verification.

* Paper: https://t.co/mq5WyFl4jb
* Lean code: https://t.co/u4UIl154AY
* DeepMind blog post: https://t.co/eBxLMPlIDJ

A huge shoutout to my amazing teammates: @tchajed,
@FerrousAndrew, Jonathan Katz, @pushmeet, @antonkovsharov, Karn Seth, Sam Schlesinger, @RandomlyWalking.

I started working on formal software verification 20+ years ago as a grad student. I was persuaded by Dijkstra and other computer science pioneers’ vision of programming backed up by mathematical theorems. At that time, we had to choose between automated code analysis algorithms that scaled but could only prove shallow properties, and deep manual verification that took decades of expert human work.

Thanks to progress in AI for mathematical reasoning, we don’t have to make that choice any more. Over four weeks in October 2026, AlphaProof Code agents generated more than 6 million lines of machine-checked Lean proof. We proved deep computational and symbolic security properties of the entire protocol core of Signal, all the way down to the ARM machine-code level.

This is just the first step, of course, because our goal is nothing short of verifying the world’s software infrastructure. Most cyberattacks, including the AI-powered ones recently in the news, happen because of preventable code bugs. I expect that agents for formal code analysis will eliminate these bugs one category at a time.

As our paper explains in more detail, this doesn’t at all mean humans are out of the picture! Proofs are now largely automated, but human experts will need to decide what is proved with AI assistance. AlphaProof Code offers an early glimpse of what such human-AI collaboration could look like, but many other proposals of this nature will emerge.

We have released all our Lean specs and proofs and plan to work with the open-source community on improving them. If you are building critical software and would like to apply these ideas, come talk to us!

[Ali Ghodsi @alighodsi]

@hwchase17 bro, databricks very much is databricks for agents

[Ali Ghodsi @alighodsi]

I don't think we should fight. The two companies are complementary. Since you're not doing this AI stuff, I really think Snowflake should consider getting its AI tokens through Databricks. You can get both cheap OSS models as well as frontier (OAI/Ant/Gemini/Grok) models from our platform. It all goes through Unity Gateway which has Smart Routing, Budgets, and Access Control. You can even use Omnigent with any harness, including CoCo. Most of our customers used this to reduce token spend ~30% last few months while token count went up. Genie lets you then track, control, manage that spend, all your financials, marketing, etc. Quick POC?

[Sundar Pichai @sundarpichai]

I did say we'd be releasing our models periodically...introducing Gemini 4 Carbon, our next frontier model.

Following Argon's release just two weeks ago, Carbon shows the pace at which we’re pushing the frontier, it ranks #1 on the recently updated v5.0 of @ArtificialAnlys Intelligence Index at 59. It also leads across key cybersecurity evaluations scoring 88.2% on SEC-bench Pro for finding vulnerabilities, 54.8% on ExploitGym for exploitation, and 71.0% on CWE-bench for patching.

We’re seeing significant improvements in long-horizon tasks even compared to Argon, and we’ve strengthened frontier safeguards.

As we did with Argon, we’re going to roll it out gradually + responsibly. Some trusted cyber defenders will get access starting this week. We'll be expanding it to more partners through our Fairwind Program, ahead of wider availability soon, so hang tight!

[benahorowitz.eth @bhorowitz]

https://t.co/kiJ0J1dIJ6

[Matei Zaharia @matei_zaharia]

Here’s how we’re building memory in Genie and the new Agent Bricks Memory service you can use in your own agents. MemCurrent automatically synthesizes new memories from traces to produce better results than alternatives.

[Veronica Qing Lyu @veronica3207]

Check out our latest research: MemCurrent, a persistent agent memory architecture, which consistently stays on the cost-accuracy Pareto frontier on our internal and three public benchmarks. Try it in the new Agent Bricks Memory and Genie 🚀

[Jonathan Frankle @jefrankle]

Databricks is investing in New York for the long haul. Very excited to continue growing our AI research lab in NYC, now one of the largest in the city!

[Ivan Zhou @ivanzhouyq]

Over the course of the past year, my research team at @databricks has completely shifted from manually running every experiment to having a swarm of agents run experiments for us. As a result, the training workloads of my research team have skyrocketed and continue to rise.
Fortunately, we have a very scalable GPU infra named AIR. Through AIR, our agents can easily provision GPU endpoints for training models of various sizes (<1B to 1T+) and monitor their status.
The agent swarm + AIR give us an infinite amount of execution power, so our focus has shifted towards defining crystal-clear goals and reviewing the outcomes. Our models come out more quickly, the quality goes up, the speed increases, and new products also get launched more quickly…
AIR is now available for all @databricks customers to use. We dogfood our own product first ;) Now you should be able to unleash your AI research team with infinite execution power as well.

[Mark Zuckerberg @finkd]

Introducing Muse Wonder, our new family of frontier models with state of the art performance for personal and professional agentic work.

[Ali Ghodsi @alighodsi]

All seven Databricks co-founders began our journey together at UC Berkeley. We started the company in a room in Soda Hall, moved to a small office on Addison Street, and spent our first few years in Berkeley before eventually heading to SF. Two of our co-founders are still on the faculty.

Today we announced Databricks Field at California Memorial Stadium, our first collegiate athletics sponsorship.

Berkeley shaped everything about how this company thinks, and this is our way of investing in the next generation of students and builders who'll do their best work on that campus.

Go Bears!

https://t.co/JO3aBx99Z5

[Patrick Wendell @pwendell]

Today we rolled out Astra to every engineer at Databricks (N=~3500). Some notes that may be helpful to others:

1. Astra unambiguously out performs our previous highest-end models (Opus 5, Sol 5.6) on highly complex tasks, especially those related to high level system design or long range horizontal tasks.

2. Engineers given Astra increased overall coding spend by around 60% compared to baseline.

3. It is not clear Astra meaningfully improves on medium/low complexity coding tasks compared to earlier models. We suspect those tasks are mostly saturated (i.e. perfectly executed) by existing models.

4. We learned above by piloting Astra with around 200 users to gain signal on both quality and cost. We use Unity Gateway to do cohort-based experiments for all new models.

5. We give engineers a sub-budget specific to Astra to encourage them to use Astra selectively on complex tasks while preferring lower cost models for everyday tasks. Our engineers are able to mix-and-match tools and models within their overall budget envelope (we also allow for increased budgets through various mechanisms). These budgets are defined in Unity Gateway and regularly revisited.

Note: We do not have robust comparisons of Astra-vs-Fable because we have net yet rolled out Fable widely due to data retention policies.

[Ivan Zhou @ivanzhouyq]

We evaluated GPT 6 Astra at @databricks and it claims the new SOTA on our OfficeQA Pro & Pro V2 benchmarks, using our Genie harness. It also improves significantly from gpt 5.6 sol on the $ per task. Throughout our benchmarks, it shows a clear step up on data reasoning and document understanding for enterprises.
It also has become my daily driver on @omnigent_ai. It is a great model to collaborate with and get things done reliably.
Congrats to @OpenAI. The model will come to our Unity Gateway and smart routing soon!

[Databricks @databricks]

Claude Fable 5.1, @AnthropicAI's most capable model, is available as a day 0 release on Databricks!

Claude Fable 5.1 joins Claude Opus 5, Claude Sonnet 5, and 30+ open-source and frontier models on Databricks.

With Unity Gateway, you can now connect Claude Fable 5.1 to your coding agents and get smart routing, centralized cost controls, and observability in a single pane of glass: https://t.co/blVEngTQtx