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Announcement reads
Announcement posts on Yomu, summarised newest first.
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Announcement posts
Announcing the Agentic Resource Discovery specification
Agentic Resource Discovery (ARD) is an open specification for publishing, discovering, and verifying AI capabilities across the web, addressing the lack of a standard that works across organizations and ecosystems. It uses organization-hosted catalogs, including an ai-catalog.json file at a well-known domain path, plus federated registries that crawl, index, and return capabilities with trust metadata. Clients can query a registry with plain-language intent or fetch a known catalog directly, verify the publisher’s cryptographic identity, then connect at runtime through the capability’s native protocol or API. Google Cloud is bringing ARD to Gemini Enterprise Agent Platform through Agent Registry, which supports hosted discovery and governance features including namespaced URNs, egress policies, specification pinning, and Agent Identity trust-manifest verification. The specification is available under Apache 2.0, with native Agent Platform support planned in the coming months.
Building AI Agents for AR Glasses and XR Devices with NVIDIA XR AI
Building AI experiences for augmented reality and wearable devices requires bridging hardware with live media streams, multimodal models, enterprise tools, and runtime infrastructure. NVIDIA XR AI provides an open-source, modular framework connecting extended reality headsets and smart glasses to GPU-accelerated services across cloud, edge, and workstations. In this architecture, camera frames and microphone audio ingest into an XR Media Hub that routes data while keeping raw video pixels in shared memory to minimize overhead. The ecosystem uses NVIDIA Cosmos models for vision-language grounding, NVIDIA Nemotron models for reasoning and tool invocation, and the Model Context Protocol for enterprise integrations. Optional agent orchestration via NVIDIA NeMo Agent Toolkit and spatial streaming through NVIDIA CloudXR support complex workflows across healthcare and manufacturing.
Enabling Governed Vibe Coding for Enterprise Apps on Databricks
Databricks introduces three capabilities intended to bring vibe coding to enterprise applications, where speed alone does not provide business-data context, deployment safety, or cost control. App Spaces lets admins define resource and data access, on-behalf-of-user API scopes, and security policies for groups of apps, with each app inheriting those settings. Genie App Builder turns plain-language descriptions into working internal apps through generated plans, live previews, AppKit, and awareness of workspace data assets and Unity Catalog semantics. Serverless micro apps run in isolated lightweight virtual machines, start quickly when needed, scale to zero while idle, and use usage-based rather than reserved-capacity infrastructure. Together, the capabilities are presented as a way for business-proximate users to build on enterprise data while organizations apply consistent governance and support broader app portfolios; all three are coming to Databricks Apps, with private previews coming soon.
Introducing OpenSharing: the Next Evolution of Delta Sharing for the Agentic Era
OpenSharing is presented as the next evolution of Delta Sharing, extending an open zero-copy data-sharing protocol from tables and files to models, agents, semantic context, unstructured data, and reusable AI logic. The protocol is now an independent open-source project hosted by the Linux Foundation, while Databricks OpenSharing adds Unity Catalog governance and audit logging, Marketplace discoverability, and enterprise features. Genie Agent Sharing supports governed AI experiences across organizational boundaries, with controls for proprietary instructions, data access, daily prompt quotas, and row exports. SecureConnect removes per-recipient firewall changes through a Databricks-managed proxy, while Global Distribution uses local replicas to reduce egress fees and latency. The launch also supports Apache Iceberg REST Catalog API, external catalogs, and on-premises storage partners; providers define shares in Unity Catalog, recipients query live data through existing tools, and governance enforces access controls.
Announcing Apps on Databricks Marketplace
Databricks announces the Public Preview of Apps on Databricks Marketplace, allowing customers to discover, install, and run third-party data and AI applications inside secure Databricks workspaces. The offering addresses procurement challenges involving data movement, lengthy security reviews, custom integrations, and fragmented identity management by bringing applications to the customer’s data. Installed apps run in isolated sandboxes within the consumer’s Databricks account, inherit Unity Catalog governance, and use dedicated serverless compute with consumer-controlled external access through Serverless Egress Gateway policies. Providers can publish closed-source containerized apps once for no-egress distribution without maintaining per-customer infrastructure, while applications connect natively to services including SQL Warehouse, Lakebase, Model Serving, and Foundation Model APIs. The Public Preview launches with 20 partners, and planned additions include bundled assets, provider analytics, and commercial monetization.
Introducing OpenSharing SecureConnect
OpenSharing SecureConnect addresses the networking burden of sharing live data from provider storage behind private networks, where providers and recipients otherwise exchange firewall and egress details manually. It is a Databricks-managed proxy that routes recipient storage access through Databricks endpoints after a one-time provider setup, while the data remains in the provider’s bucket. Providers allowlist Databricks Serverless Data Plane endpoints and enable SecureConnect for a metastore; serverless recipients require no configuration, while classic and open recipients allowlist stable inbound IPs. Optional NCC provides private link connectivity, and mutual TLS is available for recipients. SecureConnect is in Public Preview and supports cross-region, cross-cloud sharing plus customer-managed and Databricks Default Storage.
Build On-Device AI Companions with the NVIDIA ACE Game Agent SDK and Unreal Engine 5 Plugins
Game developers integrating on-device artificial intelligence companions face challenges such as conversational latency, state synchronization, and loop prevention. NVIDIA announced the open-source ACE Game Agent SDK alongside a suite of Unreal Engine 5 plugins to streamline native, hardware-accelerated character pipelines. The SDK offers Agent, Chat, and RAG APIs, enabling characters to execute multi-step tool-assisted reasoning and query game databases. Unreal Engine 5 plugins introduce local runtime models including nemo-conformer-ctc-120m for speech recognition, Qwen 3.5 4B for language generation, and Chatterbox Turbo 350M for speech synthesis. Additional tooling expands to motion generation via Animotive Kimodo and rendering updates with the DLSS 4.5 Unreal Engine plugin.
What’s new with Unity Catalog at Data + AI Summit 2026
At Data + AI Summit 2026, Unity Catalog announcements position the catalog as a runtime governance layer for enterprise data and AI, organized around control, context, and choice. Control additions include Unity AI Gateway for governing models, agents, MCP services, skills, and tools; contextual service policies can allow, deny, or require approval for runtime actions, while budgets, hard caps, tracing, and guardrails address spend, investigation, and safety. Context additions include Glossary and Domains for business meaning and scoped asset organization, plus Metrics that standardize KPIs for SQL, BI tools, APIs, and agents; Genie Ontology is described as a continuously learned enterprise context layer. Choice additions span cross-cloud and cross-region addressability, managed disaster recovery, Delta and Iceberg interoperability, multimodal and geospatial types, and open sharing of data, AI assets, and applications across organizations.
Agent Bricks: Data + AI Summit 2026
At Data + AI Summit 2026, Databricks announced Agent Bricks as a comprehensive developer platform for building and operating agents, extending a product launched the previous year. The announcement frames the core agent loop as only 1% of the work, with token capacity, deployment, security, evaluation, monitoring, context, and sharing forming the remaining infrastructure burden. Agent Bricks addresses choice, context, and control through support for multiple proprietary, open-source, and custom models, any agent harness, MCP-connected data, Genie Ontology, managed memory, document intelligence, sandboxes, and governed tools. Unity AI Gateway adds catalogs, fine-grained access controls, budgets, traffic routing, contextual policies, monitoring, and registry support for agents, tools, and models. Databricks says more than 100,000 agents have been built and customers including AstraZeneca, 7-Eleven, Fox Corporation, and Block have shipped agents on the platform.
AI governance at Data + AI Summit 2026: What’s new with Unity AI Gateway
Databricks announces new Unity AI Gateway capabilities for governing enterprise AI as organizations operate multi-model, multi-agent, and multi-vendor estates connected to models, MCP services, APIs, and tools. The update adds unified spend visibility, granular attribution, hard spend caps, and smart routing, alongside Unity Catalog support for registering and governing models, MCP services, agents, and skills. Contextual Service Policies, in Beta, can allow, deny, or require approval for actions based on users, agents, models, tools, services, or request and response contents, with guardrails for risks such as PII exposure and prompt injection. The announcement also covers end-to-end tracing, coding-agent analysis with Genie, incident investigation with Lakewatch, ecosystem integrations, and Managed Omnigent on Databricks in Beta.
Lakeflow: A new era of agentic data engineering
Databricks announces a major evolution of Lakeflow, its unified platform for data engineering across ingestion, transformation, and orchestration, with capabilities centrally governed by Unity Catalog. Genie Code and generally available Lakeflow Designer support agentic and no-code pipeline development, while Genie ZeroOps monitors production assets, analyzes failures, proposes fixes, and validates them in a governed sandbox before human approval. Lakeflow Connect expands to more than 100 managed connectors, and Zerobus Ingest adds Kafka-compatible, gRPC, REST, SDK, and OpenTelemetry interfaces for high-volume event ingestion. Real-Time Mode for Spark Declarative Pipelines reaches Public Preview with end-to-end latency as low as 5 milliseconds, alongside declarative APIs and expanded Lakeflow Jobs integrations. The release also adds data-readiness triggers and external orchestration for systems including Snowflake, REST APIs, Slack, and PagerDuty.
Introducing Genie ZeroOps: Put your data and AI operations on autopilot
Genie ZeroOps is an autonomous background agent for monitoring and operating data and AI assets, including jobs, pipelines, tables, and ML models. It continuously detects visible and silent failures, uses Unity Catalog lineage and platform observability to assess root causes, generates remediation through development workflows, and verifies fixes in isolated sandboxes. These environments use shallow, zero-copy table clones, scoped permissions, and network isolation, so proposed changes run against real data without touching production or applying anything before approval. For ML workloads, the agent can diagnose degraded predictions, train a candidate on corrected features, evaluate it against the production model’s existing eval suite and criteria, and support live-traffic ramping when it is measurably better. Genie ZeroOps is entering private preview in the coming weeks, initially supporting jobs, pipelines, tables, and ML workloads; Apps and Lakebase databases are on the roadmap.
Announcing Lakebase Search: agent-native retrieval built into Lakebase Postgres
Lakebase Search is a beta offering on AWS and Azure that adds hybrid vector and full-text retrieval to Lakebase Postgres. It uses the lakebase_vector and lakebase_text extensions to keep retrieval, memory, operational data, and hybrid search in one backend. lakebase_vector retains pgvector types and operators, applies RaBitQ clustering and compression for 32x smaller indexes, and targets more than 1B vectors, while lakebase_text replaces GIN with object-storage-optimized BM25 ranking. A tiered cache keeps hot data on NVMe and places colder data in object storage; the source reports lower memory needs, faster index builds, and cold-cache startup than standard pgvector HNSW in its LAION-100M benchmark. The extensions also combine vector similarity and keyword relevance with reciprocal rank fusion in a single SQL query, enabling joins and tenant filtering alongside transactional workflows.
Announcing the new Databricks Startup Program
The Databricks Startup Program has been updated for venture-backed, early-stage startups building with data and AI. Qualifying companies can receive up to $200,000 in credits across Databricks and Neon, along with hands-on technical guidance, partner and community access, and connections to go-to-market teams and founder communities. The program is aimed especially at startups that recently raised institutional funding from pre-seed through Series A, and it is intended to provide an app backend, data, and AI stack from idea through product-market fit. Databricks and Neon are presented as providing a database and access to foundation models on day one, while Databricks supplies analytics, data warehousing, AI systems, and enterprise-grade governance as companies launch. Applications are available through the Databricks Startup Program.
Unifying Data and Governance in the Agentic Era: What’s New with Azure Databricks
At Data + AI Summit 2026, Azure Databricks announced capabilities aimed at moving enterprises from experimental AI pilots to production-grade automated workflows by unifying data, productivity tools, marketing, and governance on Azure. Its Agentic Data foundation introduces LTAP, combining analytical data, streaming pipelines, and live application transactions in one lakehouse storage copy; Lakebase adds a managed serverless Postgres engine with copy-on-write branching, while Lakehouse//RT targets millisecond responses for high-concurrency workloads. Genie integrations for Microsoft Teams, M365 Copilot, Excel, and SharePoint bring governed lakehouse intelligence and ingestion into daily work, alongside tools for agents, applications, pipelines, and autonomous operations. CustomerLake adds Profile Agents and Campaign Agents for customer profiles and personalization, while Genie Ontology and Unity AI Gateway provide semantic context, rate limits, content filtering, and spend controls.
Introducing Genie One, Genie Agents, and Genie Ontology
Databricks announces Genie One, Genie Agents, and Genie Ontology to help enterprises answer business questions and act on data whose context is scattered across dashboards, queries, documents, tickets, and chats. Genie One connects data and business tools through Lakehouse federation, Lakeflow Connect, native integrations, Slack, Teams, mobile apps, schedules, alerts, document creation, custom skills, and MCP support. Genie Agents evolve Genie Spaces into domain-specific agents that can reason over structured and unstructured data, execute multi-step workflows, and be created from a prompt. Genie Ontology builds a permission-aware living graph from enterprise assets, weighting sources by authority, usage, certification, and freshness. In an internal 28-question benchmark, Genie answered 84.5% correctly on the first attempt and delivered twice the speed of the strongest coding agent.
Accelerate search queries with full-text search indexes on Databricks
Databricks introduces full-text search indexes in Beta on Databricks Runtime 18.2 to accelerate substring and keyword queries on large open-format tables without changing their layouts. The indexes tokenize text columns into a compact lookup structure mapping tokens to matching rows; at query time, the engine uses it to identify candidate files and skip most of the table. They are maintained asynchronously, require no query hints, preserve complete results when stale by scanning indexed and non-indexed data as needed, and support Unity Catalog managed Delta and Iceberg tables on serverless and classic compute. A Trust and Safety team reported a substring search running more than 100x faster on a petabyte-scale table, while Liquid clustering remains complementary because it optimizes column-value filters rather than text within fields.
Introducing CustomerLake: The Agentic CDP embedded in Databricks
Databricks announces CustomerLake, an Agentic Customer Data Platform embedded natively in its lakehouse, bringing Customer 360, identity resolution, audience building, campaign automation, activation, and personalization alongside governed data and AI models. The announcement addresses fragmented identities, stale audiences, manual campaign workflows, and the duplication and governance burden created by separate martech systems. CustomerLake uses Unity Catalog and Lakehouse Federation to access customer data across Databricks, Snowflake, Google BigQuery, cloud object storage, operational databases, and other enterprise systems, while Profile Agents create business-ready profiles and Campaign Agents build audiences, recommend actions, activate channels, and optimize engagement. Its operating model is described as embedded, democratized, and autonomous, with Agentic Identity Resolution combining deterministic, probabilistic, and agentic workflows. CustomerLake is now available in Private Preview and launches with an open partner ecosystem.
Introducing Lakehouse//RT: Real-Time Performance on a Unified Lakehouse
Databricks introduces Lakehouse//RT, a real-time data warehouse designed for operational analytics, BI, app serving, and observability workloads. It is powered by Reyden and is intended to deliver millisecond performance directly on lakehouse data without copying it into a separate serving layer. Preview participants saw up to 16x better performance, with response times as low as 10ms on smaller datasets, sub-100ms on larger ones, and sub-100ms latency at 12,000 queries per second on standard analytical benchmarks. Tests covering concurrency, dataset scale, and complex TPCDS queries report low latency where alternatives slowed or failed. Lakehouse//RT is in Beta for select read-only workloads, with incremental autoscaling and automatic baseline compute sizing.
Uber Advertising Launches New Solutions to Connect Brands On & Off Uber
Uber Advertising has launched new solutions that connect brands with consumers across Uber rides, Uber Eats, and external channels. Offers on Uber include Destination Offers, Homescreen Ride Offers, Ride Offers on Journey, and planned Sponsored Upgrades, combining trip context with incentives and premium ride upgrades. On Uber Eats, Brand Takeover, Item Showcase, and Offers Spotlight provide prominent placements, shoppable item features, and targeted offer visibility during discovery and ordering. Offsite Ads extends campaigns to Meta and Google Shopping, using observed Uber behaviors to target audiences and connect external engagement with orders and store visits; the program is being piloted with selected advertisers. Reported findings include 81% openness to offers while riding, a 45% higher click-through rate for one Miller Lite campaign, and double-digit item attachment after Item Showcase views, with results varying by campaign.
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