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

[Databricks @databricks]

#DataAISummit 2026 is officially underway!

This week, thousands of data and AI leaders, engineers, analysts, architects, and builders are coming together in San Francisco to explore the future of apps and agents through keynotes, technical sessions, training, and hands-on experiences.

We'll be sharing announcements, highlights, and moments from across the event all week long.

Not in San Francisco? Join the virtual experience here: https://t.co/ehV4fgucge

[Ali Ghodsi @alighodsi]

Check out Omnigent, an open source harness that lets you use all the existing code harnesses (Claude Code, Codex, OpenCode, pi), collaborate and share sessions in many modalities (e.g. Slack/Teams, cli, webui), while having a fine grained security model that really tightens the control on what agents can do/not do.
https://t.co/FRLsbUIxZn

[Rishabh Singh @rishabhs]

We have found composing multiple agents for performing complex e2e tasks drives higher accuracy and robustness, and now Omnigent allows everyone to easily build and use multi-agent workflows securely. The multi-user collaboration feature is also very cool, try it out!

[Matei Zaharia @matei_zaharia]

Really excited to open source a new project: Omnigent, a meta-harness for AI agents.

It lets you build multi-agent coding and custom agents, sitting above Claude Code, Codex, Pi, and agent SDKs to let you compose them. It also adds live collaboration and rich control policies.

[Matei Zaharia @matei_zaharia]

Really excited to open source a new project: Omnigent, a meta-harness for AI agents.

It lets you build multi-agent coding and custom agents, sitting above Claude Code, Codex, Pi, and agent SDKs to let you compose them. It also adds live collaboration and rich control policies.

[Databricks @databricks]

Google's Gemini 3.5 Flash is now available on Databricks!

@Google just released Gemini 3.5 Flash, and Databricks is among the first outside the Gemini Enterprise Agent Platform to offer it. Build and scale agentic AI applications on your enterprise data with the governance and operational tooling your teams need for production.

Congrats to the Google Gemini team on this release. We can't wait to see what you build with it! https://t.co/a5X6WjpfLJ

[Ashutosh Baheti @abaheti95]

In 1945, Vannevar Bush imagined a machine to extend a scientist's memory. He called it the MemEx.

80 years later, we built one for LLM agents.

Tool outputs become Python objects; only print statements reach the model's context.

🧵 https://t.co/YyrGsn3TB7

[Julia Neagu @juliaaneagu]

I'm building a new team at @databricks AI Research and we're hiring.

We're focused on one of the hardest open problems in AI right now: how do you measure and continuously improve agents that operate on enterprise data at scale. We're looking for founding engineers to build the flywheel that turns evaluation results directly into better agents — from development and training all the way to production.

If you want to work on problems that actually matter at the frontier of AI research, I'd love to talk.

Link in comments 👇

[Yuchen Jin @Yuchenj_UW]

Super cool work from Databricks AI research team.

Data agents are harder than coding agents. Coding agents have verifiable tests. Data agents have to find “truth” across millions of tables, docs, dashboards.

Databricks Genie got to 91.6% accuracy, while the leading coding agent only got 32% on enterprise data analysis tasks.

Specialized knowledge search + Parallel Thinking + Multi-LLM is the key.

Databricks has an amazing research team, and I've been enjoying working with them!

[Databricks AI Research @DbrxMosaicAI]

Coding agents operate in relatively static environments. Data agents do not.

To answer complex enterprise questions, data agents need to discover relevant assets across tables, dashboards, notebooks, and documents, resolve conflicting business context, and reason without deterministic tests.

Our latest Databricks AI Research blog shares how Genie addresses these challenges through specialized knowledge search, parallel thinking, and Multi-LLM designs, improving accuracy from 32% to over 90% on real-world data analysis tasks. https://t.co/0L3FkSAIcM

[Rishabh Singh @rishabhs]

There are still a lot of research challenges we need to tackle to further improve Genie, and it has never been a more exciting time to explore research in this area. We are hiring in the Databricks AI research team, please join us!

[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]

Finally, we observe that different LLMs offer complementary strengths. Genie’s design leverages this by allowing us to use different models for specific components, all driven by GEPA-optimized prompts! This flexibility is key to finding the best model combinations for jointly optimizing accuracy, latency, and cost.

[Matei Zaharia @matei_zaharia]

Genie has transformed how Databricks users work with data, with 3x the accuracy of generic agents. We're sharing some of the research behind it and what makes building data agents challenging. Super proud of our research team's impact with this! https://t.co/eLB2ElVo8S

[Rishabh Singh @rishabhs]

Genie now enables all business users to ask complex questions across both structured and unstructured data that require rich semantic understanding. Super excited to see how users will use Genie to help them with all the data exploration and analysis in the most natural way!

[Ali Ghodsi @alighodsi]

𝐆𝐞𝐧𝐢𝐞 is now the most important way to do data analysis in Databricks. What's unique about it is its ability to extract semantics from your entire Lakehouse, enabling it to answer complex data questions that cripple agents without a deep data understanding. We've now added a Mobile version, added Unstructured data processing, as well as enabled it to operate on all your dashboards and notebooks. Check it out:

https://t.co/bqPvg2lYS7

[Matei Zaharia @matei_zaharia]

GPT 5.5 and Codex are now available and manageable on Databricks! Both support Unity AI Gateway so you can manage access and costs, add guardrails, secure access to MCPs centrally, and audit usage. https://t.co/FZ5XRKlCxh

[Gal Oshri @GalOshri]

The numbers are wild, and the best part is what's behind them: hearing how data teams are shipping dramatically faster (and having more fun along the way)!

[Rishabh Singh @rishabhs]

Genie Code now writes more code than Human-written code on the Databricks platform!

[Ali Ghodsi @alighodsi]

Just got this internal email on Genie Code progress! For the first time, we have MORE code written by Genie Code than by humans on the platform.