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“With coding agents, you’re putting this jetpack on and going 10,000 miles an hour, doing things you were never able to do before.” In this conversation, OpenAI’s Peter Steinberger 🦞 and Tibo join Databricks Co-founder Patrick Wendell to unpack what comes next as agents move beyond code authoring into... show more

Databricks

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20,061 просмотров • 9 дней назад •via X (Twitter)

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As agentic automation helps developers get more done, the role is evolving toward higher-level decisions about what to build, how systems should work, and where taste and creativity matter most. Databricks Co-founder Patrick Wendell, OpenClaw creator Peter Steinberger 🦞, and Codex engineering lead Tibo discuss what that shift means for enterprises building, governing, and scaling fleets of AI agents in production. Watch the full conversation:

As agentic automation helps developers get more done, the role is evolving toward higher-level decisions about what to build, how systems should work, and where taste and creativity matter most. Databricks Co-founder Patrick Wendell, OpenClaw creator Peter Steinberger 🦞, and Codex engineering lead Tibo discuss what that shift means for enterprises building, governing, and scaling fleets of AI agents in production. Watch the full conversation:

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Databricks CEO and co-founder Ali Ghodsi joined Bloomberg Technology to discuss the launch of Genie Code, an autonomous AI agent built specifically for data teams. "Just six months ago, we were talking about autocompleting code — now agents automate the code. The new question is: how do we take the code that’s been written into production and make sure we're monitoring it?" Watch the full conversation

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Databricks

63,207 просмотров • 4 месяцев назад

After decades of building databases, Databricks VP of Engineering Nikita | Scaling Postgres believes it's time to rethink one of their fundamental assumptions. In this episode of the 𝘐𝘵'𝘴 𝘈𝘣𝘰𝘶𝘵 𝘋𝘢𝘵𝘢 podcast, Nikita discusses why separating compute and storage is the next major architectural shift for operational databases, how storing data on object storage opens it to downstream systems, and why the rise of AI agents is changing how developers build software—and what they need from a database. Watch the full conversation:

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Databricks

10,460 просмотров • 1 месяц назад

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Databricks

12,707 просмотров • 4 месяцев назад

Excited to share this conversation with Peter Steinberger 🦞! We recorded it in SF a couple weeks ago, before he joined OpenAI. Peter is one of the most creative builders I know. We talked about OpenClaw🦞 🦞, where his ideas come from and how you can just… build things! Full interview 👇

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OpenLedger

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Aaron Levie

17,543 просмотров • 4 месяцев назад

This is probably the biggest news yet in software going headless, and will bring knowledge work agents to the masses. The new ChatGPT agents have access to any of the tools and data you want to work with, with complete coding and tool use available to them. Here's an example of a custom sales assistant agent uses Box as a knowledge source for accessing enterprise content securely to answer questions and generate new content on the fly. The workflows can obviously vastly far more complex as the agent can use any of the tools within Box available via MCP and CLI. This precisely what agents will start to look like for knowledge work. You'll be able to spin them up in the foreground or background to help augment work. Big opportunity right now for headless platforms, and for all the new builders and designers of these agents in the enterprise.

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Aaron Levie

420,133 просмотров • 4 месяцев назад

Without proper governance, an AI agent might autonomously access sensitive data, expose personal information, or modify sensitive records. In our new short course: “Governing AI Agents,” created with Databricks and taught by Amber Roberts, you’ll design AI agents that handle data safely, securely, and transparently across their entire lifecycle. You’ll learn to integrate governance into your agent’s workflow by controlling data access, ensuring privacy protection and implementing observability. Skills you'll gain: - Understand the four pillars of agent governance: Lifecycle management, risk management, security, and observability - Define appropriate data permissions for your agent - Create views or SQL queries that return only the data your agent should access - Anonymize and mask sensitive data like social security numbers and employee IDs - Log, evaluate, version, and deploy your agents on Databricks If you’re building or deploying AI agents, learning how to govern them is key to keeping systems safe and production-ready. Sign up here:

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Andrew Ng

78,182 просмотров • 10 месяцев назад

For a long time, software was limited by how fast people could write code, and how good that code was. As models have improved, that constraint has largely disappeared. Now the bottleneck is access: what surfaces can your agents actually reach? Those interaction layers sit on top of coding agents, the kernel that turns prompts into real-world impact. With Notion's custom agents, we pushed this further. They adapt to your work style as you collaborate with them, using the same deterministic logic that powers coding agents. Simon Last sarah guo

For a long time, software was limited by how fast people could write code, and how good that code was. As models have improved, that constraint has largely disappeared. Now the bottleneck is access: what surfaces can your agents actually reach? Those interaction layers sit on top of coding agents, the kernel that turns prompts into real-world impact. With Notion's custom agents, we pushed this further. They adapt to your work style as you collaborate with them, using the same deterministic logic that powers coding agents. Simon Last sarah guo

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This week, Co-founder and Head of Research Ethan Jackson Neurmal//Theoriq returns with a full end-to-end demo of the OLP Swarm in action. He walks through how key components—pool pulses, Statistical Agents, Policy Agents, and LP Agents—work together to process data and trigger an onchain action. 🔫 He also showcases the Swarm dynamically responding to a natural language prompt and adjusting its liquidity position in real time. Watch the full demo below.

This week, Co-founder and Head of Research Ethan Jackson Neurmal//Theoriq returns with a full end-to-end demo of the OLP Swarm in action. He walks through how key components—pool pulses, Statistical Agents, Policy Agents, and LP Agents—work together to process data and trigger an onchain action. 🔫 He also showcases the Swarm dynamically responding to a natural language prompt and adjusting its liquidity position in real time. Watch the full demo below.

Theoriq

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Introducing Databricks Document Intelligence: a research-specialized layer that turns raw enterprise documents into structured data your agents can actually reason over. Across our benchmarks, Document Intelligence delivered the highest end-to-end parsing and extraction quality at 6-8x lower cost, with a 16% average performance gain across every agent framework tested, just from better parsing. How to get started:

Introducing Databricks Document Intelligence: a research-specialized layer that turns raw enterprise documents into structured data your agents can actually reason over. Across our benchmarks, Document Intelligence delivered the highest end-to-end parsing and extraction quality at 6-8x lower cost, with a 16% average performance gain across every agent framework tested, just from better parsing. How to get started:

Databricks

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Databricks Co-founder and Chief Architect Reynold Xin announces LTAP (Lake Transactional/Analytical Processing) at #DataAISummit 2026. LTAP is a new data processing architecture that unifies OLAP and OLTP on a single copy of data in the lake, eliminating ETL, replicas, and pipelines by design.

Databricks Co-founder and Chief Architect Reynold Xin announces LTAP (Lake Transactional/Analytical Processing) at #DataAISummit 2026. LTAP is a new data processing architecture that unifies OLAP and OLTP on a single copy of data in the lake, eliminating ETL, replicas, and pipelines by design.

Databricks

27,639 просмотров • 2 месяцев назад

“I am going to probably use $300M of Anthropic this year at Salesforce.” - Marc Benioff “ These coding agents are awesome. Anthropic is awesome. Coding, everything's going to be cheaper to make, it's more efficient. I can do things that I just could not do before. I can go faster than ever before. I can implement my software and sell it at the same time. I've never been able to do that before. Today, I have humans, agents, and headless platforms all interoperating, never before. So the opportunity for my own company and the efficiency that I have in my own company, in service and support, in distribution and marketing, across the board, is unprecedented. What I can do for our customers, unprecedented. And, to that point, my gosh, have you seen Anthropic? It is a rocket ship that will not stop.”

“I am going to probably use $300M of Anthropic this year at Salesforce.” - Marc Benioff “ These coding agents are awesome. Anthropic is awesome. Coding, everything's going to be cheaper to make, it's more efficient. I can do things that I just could not do before. I can go faster than ever before. I can implement my software and sell it at the same time. I've never been able to do that before. Today, I have humans, agents, and headless platforms all interoperating, never before. So the opportunity for my own company and the efficiency that I have in my own company, in service and support, in distribution and marketing, across the board, is unprecedented. What I can do for our customers, unprecedented. And, to that point, my gosh, have you seen Anthropic? It is a rocket ship that will not stop.”

The All-In Podcast

229,386 просмотров • 3 месяцев назад

New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!

New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!

Andrew Ng

127,724 просмотров • 1 год назад

I’m excited to share Deputies, an open source control plane for background coding agents! The goal is simple: move agentic software development off individual laptops and into a central hub where teams can see what agents are doing, review their outputs, and keep a traceable history of the work. Inspired by systems like Inspect/Open-Inspect, Minions, and Open SWE, I built Deputies on top of fks's Flue Framework. It’s designed to run in your own infrastructure and trust boundaries, with Slack/GitHub integrations and Daytona + Docker sandbox support.

I’m excited to share Deputies, an open source control plane for background coding agents! The goal is simple: move agentic software development off individual laptops and into a central hub where teams can see what agents are doing, review their outputs, and keep a traceable history of the work. Inspired by systems like Inspect/Open-Inspect, Minions, and Open SWE, I built Deputies on top of fks's Flue Framework. It’s designed to run in your own infrastructure and trust boundaries, with Slack/GitHub integrations and Daytona + Docker sandbox support.

Sid Palas

12,852 просмотров • 3 месяцев назад