AI Agents
Recent items mentioning AI Agents across the Databricks ecosystem — releases, news, videos, and community Q&A. Updated hourly.
Databricks has significantly advanced its AI agent capabilities, notably with the open-sourcing of Omnigent, a meta-harness for orchestrating and observing multi-harness AI agents with automatic MLflow Tracing 137. This aligns with the expanded Genie family, which now includes Genie Agents, and the Unity AI Gateway for enhanced governance 8. Databricks also highlighted the use of agentic coding for stress-testing GPU reliability and recognized VisionHeight for its agentic threat intelligence in the Built-On Databricks Startup Challenge 45.
Generated daily from the 10 most recent items mentioning AI Agents. Click any [N] to jump to the source.
ReleasesDatabricks launches across the Data + AI stack in 90 seconds
Databricks announced LTAP to unify lakebased and lakehouse data, eliminating ETL and enabling a single copy of data for analytical and operational needs. They also introduced Unity AI Gateway for governance, Genie Ontology for enterprise knowledge graphs, and open-sourced Omniant for managing multiple coding agents.
Multi-Harness AI Agents Need Multi-Layer Observability: Omnigent in MLflow
Omnigent unifies multi-harness agent orchestration and now delivers automatic observability across every agent with MLflow Tracing, requiring no code changes. This post details how Omnigent in MLflow provides multi-layer observability for multi-harness AI agents.
How we keep GPUs reliable across Databricks AI
Databricks AI uses a multi-pronged approach to ensure GPU reliability, addressing crashed jobs, silent slowdowns, and numerical corruption through pre-workload validation, in-load monitoring, and inter-node fabric health checks. This system, stress-tested by diverse, large-scale workloads like RL for agentic coding, catches issues like fabric flakiness and thermal hotspots before they impact broader production.
Celebrating the Winners of the 2026 Built-On Databricks Startup Challenge
Databricks is celebrating the winners of the 2026 Built-On Databricks Startup Challenge, a global competition for early-stage B2B startups building core products on the Databricks platform. VisionHeight took the grand prize for its attack intelligence infrastructure, an innovation in agentic threat intelligence, alongside other winners in areas like web search and retail.
Launch HN: Parsewise (YC P25) – Reason Across Documents with an API
Hi all, it’s Greg and Max, founders of Parsewise here (https://www.parsewise.ai/api). Parsewise transforms a bucket of unstructured data into schema compliant data, retaining lineage for values resolved across documents. Imagine giving Claude a bunch of files and asking for a CSV or JSON output. If you have tried this, you know both the system limitations (number of files, type of inputs, cost, latency) but also the human-facing challenge of having no way to validate the results quickly. We solve both. We help tech teams simplify their unstructured data ETL, and loop in business experts for the definitions and for instant validation. Here is a video with a few use cases: https://www.youtube.com/watch?v=dbRllnnh47w Parsewise in the words of someone coming to us: ”I need to extract information from insurance policy PDFs, phone calls that have been transcribed, emails, etc. I am NOT looking for something that would just extract data point by data point, page by page into a structured well-defined schema but more something more agentic that can understand that information might be across documents and that it should reason over what to extract.” We started the company based on a decade of experience (and pain) in complex data transformation and data analysis / synthesis. Greg was building both classical ETL and implemented AI workflows at Palantir. At Bain, Max did highly complex data analysis in the financial sector, similar to many of our customers. Parsewise works by taking in a bucket of data (think hundreds or thousands of pdfs, excels etc.), and outputting schema compliant data where every single value is traceable down to word level citations across multiple documents in the bucket. We provide API customers with ways to show the lineage in their own applications, or they can use our platform for internal operations. At the core of the data processing we have self-improving agent definitions. They define the acceptable sources, the logic for resolving or combining values, and the rule for highlighting uncertainty to the end user. The underlying tech is model and cloud agnostic and can be deployed in private networks. We have seen the best results with Gemini models for visual reasoning, achieving SOTA (beating Claude Fable) on the strongest grounded reasoning benchmark we have found (Databricks OfficeQA). Notably, we focused more on the “human harness” rather than the model harness, leaning into the actual friction we saw in uptake, which is around verifiability. That means optimizing the time and clicks required to trust the outcomes. We use vLLMs for parsing, and then we use small models for efficient large scale exhaustive search. Unlike RAG, we do not sample; instead, we exhaustively find all relevant values for a given query. We use larger models for decision making around resolutions and flagging inconsistencies to users. This exhaustiveness and explicit value sourcing is unique to our platform, and it goes beyond the first step of data parsing that many existing providers cover. We would love to welcome builders and tinkerers to try Parsewise on your complex document challenges. We have a ton of ideas on how we can expand the product and make it better, but would appreciate feedback and ideas from the community! --- top comments --- [whinvik] Document parsing is top of my mind lately because in some of the areas we work on the bottleneck is starting to become being able to query documents the same way one queries an api. I keep thinking the most obvious analogue is we need some way to represent documents the same way we can represent structured data in parquet. Parquet allows easy range bases queries and there is so much tooling built around Arrow. But for documents I keep hitting a wall to figure out what the right abstractions are. Parquet allows filterable metadata. But what such metadata is there for documents. Then there is the arbitrrariness of chunking, vectorization. If we could just do this in a […truncated]
ReleasesIntroducing Omnigent: The Ultimate Meta-Harness for AI Agents
Omnigent is a new open-source meta-harness for AI agents that provides a unified interface for composition, control, and collaboration across multiple models and agent workflows. It enables stateful, data-centric policies for guardrails and allows real-time sharing and steering of live agent sessions with teammates.
EventsAll the AI Databricks Data + AI Summit Announcements you need to know | AI Newsround - June 2026
Databricks announced several new AI-focused products and updates at its 2026 Data + AI Summit, including the expanded Genie family (Genie 1, Genie Ontology, Genie Agents), the Unity AI Gateway with enhanced governance and cost tracking, and Customer Lake for an integrated CDP solution. The video also highlighted Omnigent as an early-stage open-source meta-harness for agent interoperability and the release of the capable open-source model GLM 5.2.
Agents at Work: Shipping Agentic Apps at Scale | Virtual Event
Accessing Document Presented in Demo in Get Started with AI Agents on Databricks Course
NewsDatabricks + Panther: advancing the security lakehouse
Databricks acquired Panther, a cybersecurity company that developed an AI SOC platform to make security teams smarter and faster. Panther's platform helps security teams detect and respond to threats and manage compliance by leveraging large datasets without requiring deep data infrastructure expertise.
CommunityDefending against a tidal wave of AI attacks with Lakewatch, the agentic security Lakehouse
Databricks introduces LakeWatch, an agentic security lakehouse designed to defend against AI-powered attacks by enabling security and data teams to work together. It allows for the ingestion and analysis of all security data, automates detection rule creation and incident investigation using AI agents, and breaks down proprietary data silos.
ReleasesIntroducing CustomerLake – the agentic CDP built in Databricks
Databricks introduces CustomerLake, an agentic Customer Data Platform (CDP) built within the Databricks Lakehouse, designed to power "infinity campaigns" for real-time, one-to-one customer personalization. CustomerLake features Profile Agents for automated customer 360 data creation and Campaign Agents for generating and managing continuously adapting, personalized marketing campaigns.
NewsAgentic machine learning with Genie Code (includes demo)
Databricks announces Genie Code for machine learning, an enhanced coding agent that leverages an ontology of an organization's ML projects to build, evaluate, and deploy models following team best practices. They also introduce Genie Zero Ops for machine learning, an agent that autonomously monitors production models, identifies root causes of issues, and proposes solutions for human approval.
ReleasesIntroducing Omnigent: an open meta-harness – Matei Zaharia, Co-founder and CTO, Databricks
Databricks introduces Omnigent, an open-source "meta-harness" designed to manage and build with AI agents by providing a common layer above individual agent harnesses. It enables agent composition, live collaboration, and enhanced control over cost and security through features like contextual policies and sandboxing.
What if the answer was already in your data?
Kythera Labs' AI agents, built on Databricks, now provide health system leaders with governed, trustworthy answers to strategic questions from 339 billion claims. A Louisiana health system saw 150% more visibility into patient encounters and $3.8M in estimated annualized value in 10 days.
Databricks positioned highest in execution and furthest in vision for the second consecutive year in Gartner Magic Quadrant
Databricks is recognized as a Leader in the 2026 Gartner Magic Quadrant for AI Platforms for Data Science and Machine Learning, positioned highest in execution and furthest in vision for the second consecutive year. This reflects the market shift towards deploying agentic applications that reason on governed data, enabled by Databricks' unified data, AI, and governance platform with Unity Catalog and Unity AI Gateway.
ReleasesIntroducing Lakehouse//RT and Reyden — Reynold Xin, Co–founder and Chief Architect
Databricks introduces Lakehouse//RT, a new SQL warehouse powered by the Raiden engine, designed to provide millisecond performance and massive concurrency for real-time analytics directly on data lake formats like Delta and Iceberg. This innovation aims to unify data warehousing and serving stacks, eliminating the need for separate systems and data copies.
EventsHow Mastercard standardizes on Lakebase to power agentic operations
Mastercard uses Lakebase to standardize its agentic operations, creating a shared foundation for services like the "virtual C-suite" for small businesses and secure multi-tenant solutions for thousands of issuing banks. This standardization enables rapid development of AI agents with embedded governance and trust, allowing them to learn from each other and scale effectively.
EventsUnlocking agentic data engineering with Lakeflow + Genie
The video introduces Lakeflow as a unified, open data engineering stack that simplifies data transformation, ingestion, and orchestration through declarative pipelines, no-code tools, and managed services. It also announces Genie Zero Ops, an AI agent that automates data operations by autonomously detecting, diagnosing, and verifying fixes for data incidents and PII exposures within the data plane.
ReleasesIntroducing Genie One: the AI coworker that understands your data (with demo)
Genie One is an AI co-worker designed to connect to all enterprise data and applications, providing accurate insights and automating actions by leveraging a "Genie ontology" for context. It enables users to create documents, generate forecasts, and build domain-specific agents that compute results from live operational data, rather than just reciting existing information.
EventsNo one needs to care about table formats with Databricks' Ryan Blue, creator of Apache Iceberg
Databricks announced the GA release of Iceberg v3, which unifies data layers so files can be shared across Delta and Iceberg tables without rewriting. The company is also working towards a unified metadata layer in Delta 5 and Iceberg v4, aiming for a full unification vision later this year.
EventsHow PepsiCo goes from dashboards to outcomes and deep research with Genie
PepsiCo transitioned from over 60 data lakes to a single Databricks lakehouse, consolidating 90% of its data universe onto one platform. This data foundation, combined with the Genie tool, enables PepsiCo to move from dashboards to outcomes by facilitating deep research and agentic AI for its 320,000 employees.
NewsMeet Genie: The new AI coworker that knows your business
Genie is a new AI coworker from Databricks designed to understand a company's specific business context. It aims to provide relevant and accurate information by leveraging internal data and proprietary knowledge.
NewsAI-Ready Data on Databricks: How TK Elevator Uses Context and Meaning to Make AI Agents Work
TK Elevator uses Databricks' Unity Catalog to create "AI-ready data" by harmonizing data from over 100 disparate systems, enabling a common language for their AI agents. This foundation supports predictive maintenance for elevators, empowering 25,000 service technicians with tailored support and voice debriefing capabilities.
Guide to Agentic Systems and AI Agents
Agentic AI systems are autonomous software platforms that perceive, reason, execute multi-step tasks, and learn with minimal human intervention, unlike traditional generative models. These systems use LLMs as reasoning engines with external tools and memory to complete complex workflows, with enterprise adoption spanning customer service to financial risk.
EventsAPJ Experience at Data + AI Summit 2026 - Day 2 Recap
The video highlights Databricks' focus on empowering developers and practitioners with AI capabilities, particularly through OmniGen, which provides access to various models and coding agents. It also emphasizes the company's role in democratizing AI and data, showcasing how customers are leveraging Databricks for real-time processing and agentic AI solutions.
Databricks Introduces Omnigent: A New Meta-Harness for Building and Managing AI Agents
ReleasesAnnouncing CustomerLake: the agentic CDP embedded in Databricks
Databricks announces CustomerLake, an agentic Customer Data Platform embedded within the Databricks platform. It offers a business-first UI, leverages Unity Catalog for governance, and uses agents for autonomous, personalized customer experiences.
NewsAn agentic CDP empowers marketing teams to move from static campaigns to Infinity Campaigns
Databricks introduces "Infinity Campaigns," a new marketing approach where an AI agent processes customer signals to determine the next best action and generate personalized content. This creates a continuous feedback loop where customer interactions generate new signals, feeding back into the agent for ongoing optimization.
NewsGenie Code for machine learning brings agentic development to ML workflows #databricks
Databricks Genie Code introduces agentic development for ML workflows, integrating with the complete machine learning stack. It leverages real-world ML lessons and understands team workflows to provide recommendations and assist data science teams.
NewsWhat's new in Unity AI Gateway: multi-AI governance and cost control #databricks
Unity AI Gateway provides centralized governance, security, and cost control for AI models, agents, and skills. It introduces an agent registry to unify inventory and management of all agentic assets across an enterprise.
ReleasesIntroducing Omnigent: a meta-harness to combine, control and share your agents #databricks
Databricks introduces Omnigent, an open-source meta-harness designed to combine, control, and share various AI agents by providing a common interface above existing agent harnesses. Omnigent features a runner for agent sandboxing and monitoring, and an optional server for central policy, collaboration, and a uniform interface across web, mobile, and API.
Does "move fast and break things" ruin AI agents?
TutorialsHow to Scale AI Agents Properly | Microsoft Agent Framework Foundations | Part 3
The video demonstrates building AI agent workflows and orchestrations using the Microsoft Agent Framework, covering low-level, sequential, and concurrent pipelines. It shows how to chain agents for tasks like blog writing and run them in parallel for independent processes, highlighting the framework's simplicity and speed.
How Stagwell built privacy-safe ID matching on Databricks
Stagwell built a privacy-safe ID matching solution on Databricks, leveraging Databricks Clean Rooms and Marketplace apps to securely match first-party data with identity graphs. This enables brands to create actionable audiences without exposing sensitive information or raw records.
EventsIntroducing CustomerLake: The Agentic CDP | Ali Ghodsi Databricks CEO at Data + AI Summit
Databricks introduces CustomerLake, an agentic Customer Data Platform built on the lakehouse architecture. It features a profile agent for identity deduplication using LLMs and a campaign agent for personalized, one-to-one "infinity campaigns."
EventsRecap of product announcements from Data + AI Summit 2026 | Day 1
Databricks announced several new products and features at the Data + AI Summit 2026, Day 1, including the Genetic Data Foundation, Lakehouse RT, Lake Base with disaster recovery, Lake Flow, Genie Ontology, Unity AI Gateway, Omnigent, and various Genie agents (Genie 1, Genie Code, Genie Agents). They also introduced new applications like Lake Watch for SIM and Customer Lake for CP.
EventsGenie Ontology: an automatic and secure context store | Data + AI Summit 2026 #databricks
Genie Ontology is a new Databricks feature that automatically connects to all organizational data, including external sources like Google Drive and email. It constructs a graph of important knowledge in the background, making this context available for AI agents.
What is an AI agent harness?
AI agent harnesses turn model reasoning into reliable action by providing the tools, memory, execution environments, and guardrails agents need to complete real-world tasks. Harness design directly shapes agent performance, with strong context management, orchestration, and verification often mattering as much as the underlying model.
Databricks and NVIDIA: Building for the Agentic Era
Databricks and NVIDIA are expanding their collaboration to deliver an end-to-end AI platform, accelerating model training, inference, and agentic AI development on governed enterprise data. This includes multinode training in AI Runtime, GPU support in Databricks Free Edition, Model Serving Enhancements, and support for NVIDIA Agent Toolkit and industry-specific AI frameworks.
Building an open ecosystem for AI governance with Unity AI Gateway
Unity AI Gateway now integrates with 10 security vendors to protect AI interactions and 3 identity providers to govern AI agent access. This enables centralized visibility and control over AI activity, policy enforcement, and risk management across models, agents, and tools.
Introducing the Agentic CDP: A New Species of CDP for a New Era of Agents
Databricks announces the Agentic CDP, a new species of CDP designed for the era of AI agents. It combines Golden Context, Infinity Campaigns, and native data foundation integration to power real-time, always-on 1:1 personalization.
How dbt makes agentic data pipelines trustworthy: the transformation layer's role in autonomous data systems
Databricks practitioners can now leverage dbt as the transformation layer to ensure trustworthiness in agentic data pipelines. This post argues that dbt defines correctness within autonomous data systems, enabling AI agents to run pipelines reliably.
Building the agentic data stack: A practical dbt guide for the AI era
Databricks now supports an agentic data stack. Learn how to prepare your dbt project to robustly support AI agents.
Databricks Launches LTAP: A Unified OLAP/OLTP Data Architecture
--- top comments --- [epistasis] > The New Data Foundation for the Agentic Era Look, this announcement seemed exciting, but I'm significantly less excited when I come across a completely unrelated tie-in to AI. It breaks the illusion, and I'm reminded that it's just another PR announcement, and this is probably not going to impact my life at all in any way ever. So I'm off to the next article instead of reading any more. [mohsinimam] Curious how is the final format of the data in LTAP storage - is it columnar? If so then what happens to OLTP performance - the blog and all info speaks to OLAP performance but what about your app [mathisd] > No performance tradeoffs, for any workload: Transactional workloads run in standard Postgres with full ACID semantics. Analytical workloads run across the full Lakehouse at any scale and concurrency. Each scales independently, and because there's no data movement between systems, operational and analytical results are always in sync — with no copies or shadow infrastructure. How can there be no performance trade-off if storage is handled by PostGres and there is no data movement to convert it to columnar ? This deserve a technical explanation because this seems impossible. [geophph] Lakebase + Lakehouse = Lake [drchaim] No benchmarks, no pricing, no examples..
The Comparison: Why the Alternatives Fall Short for Databricks-Native Agentic Systems
Introducing OpenSharing: the Next Evolution of Delta Sharing for the Agentic Era
When Databricks pioneered Delta Sharing in 2021, we set out to solve a problem that...
What’s new with Unity Catalog at Data + AI Summit 2026
Unity AI Gateway now extends Unity Catalog's runtime governance to AI agents, models, and tools, allowing you to govern agent actions, not just data access. Glossary and Domains provide a shared, governed source of business context for both people and agents, while a single catalog and policy set ensure consistent governance across all clouds and regions.
NewsMeet Genie: The new AI coworker that knows your business
Genie is a new AI coworker from Databricks designed to understand a company's specific business context. It allows users to ask questions in natural language and receive answers derived from their own business data.
Lakeflow: A new era of agentic data engineering
Lakeflow unifies ingestion, transformation, and orchestration under Unity Catalog, providing a single source of trusted, real-time context for agentic AI. It offers high-performance ingestion from 100+ sources, real-time streaming, visual pipeline building with Lakeflow Designer, and AI-powered authoring and operations with Genie Code and Genie ZeroOps.
Unifying Data and Governance in the Agentic Era: What’s New with Azure Databricks
Azure Databricks now offers new capabilities for unifying data and governance in the agentic era, including the industry's first true LTAP architecture, serverless Postgres database branching, and millisecond-level response times via Lakehouse//RT. These updates also bring Genie for Microsoft Teams and M365 Copilot, the new Azure Databricks Excel Add-in, and Azure Databricks CustomerLake, a lakehouse-embedded Agentic CDP.
Introducing Genie One, Genie Agents, and Genie Ontology
Genie One is now available as a data-smart AI coworker for business users, expanding Genie beyond conversational analytics with agentic actions and integrations. Genie Agents allow teams to create shareable, autonomous agents that reason over data and take action across business tools, powered by Genie Ontology's unified context layer for trustworthy, context-rich answers.
Databricks announces 2026 global partner awards
Databricks announced its 2026 global partner awards, recognizing over 60 Consulting and System Integrator and ISV partners including Accenture, Deloitte, Anthropic, and NVIDIA. This year's awards emphasize AI transformation, lakehouse modernization, Unity Catalog governance, and agentic AI at enterprise scale.
EventsDatabricks CustomerLake (Full Keynote)
Databricks announces Customer Lake, an "agentic CDP" embedded in the Databricks Lakehouse, designed to power "infinity campaigns" for real-time, one-to-one customer personalization. Customer Lake comprises Profile Agents for creating golden customer records and Campaign Agents for autonomous, continuous engagement loops.
Introducing Omnigent: A Meta-Harness to Combine, Control and Share Your Agents
Omnigent, an open source meta-harness, is now available to combine, control, and share your AI agents across various models and interfaces. It enables building agent teams, controlling them with policies, and sharing live sessions with teammates.