AI Agents

The prototyping tax is killing your AI roadmap

Platform-native agents grounded in business semantics—like Databricks Genie plus Unity Catalog—eliminate the "prototyping tax" of fragmented context and siloed domain knowledge, hitting 77% accuracy versus 56–72% for general coding agents at roughly half the cost. Abacus Insights proved the approach even in regulated settings, cutting new-client onboarding time by about 50% and manual data-mapping effort by 40% inside a HIPAA-grade environment.

Why agentics projects fail and how to fix them

Why do some Databricks agentic AI deployments succeed while so many fail? The data points to specific, identifiable failure patterns—and this post breaks down what they are and how teams can fix them.

Electric joins Databricks to bring WASM Postgres to AI agent sandboxes

Electric is joining Databricks to bring WASM Postgres and agent-focused data primitives to AI agent sandboxes. This extends Databricks' Postgres capabilities to the edge, allowing lightweight open-source

Innocent until combined: Blocking the lethal trifecta with Omnigent Contextual Policies

Omnigent Contextual Policies stop AI agent data exfiltration by tracking session context and blocking outbound steps as soon as private data access and untrusted content exposure combine. Configured by humans rather than the

Managing AI Coding Costs at Scale

AI coding tools deliver immense value: at Databricks, agentic coding has measurably...

What is an AI Assistant?

Enterprise AI assistants use large language models, retrieval-augmented generation, and agentic frameworks to execute workflows ranging from data analysis to autonomous pipeline management. Choosing the right tool requires evaluating data integration depth, governance controls, extensibility, and alignment with your team's existing workflows and skill levels.

What are Agentic Workflows?

Agentic workflows move beyond rigid, traditional automation by using autonomous AI agents that dynamically plan, select tools, and adapt to intermediate results at runtime. Deploying these workflows for enterprise efficiency and scalability requires strong governance, clear business objectives, and production-grade infrastructure.

What is Tool Calling?

Tool calling enables AI models to interact with external systems and APIs, transforming basic chatbots into agents that execute real-world actions through a structured request-and-response loop. Databricks Agent Bricks provides a governed platform to build these agents grounded in enterprise data, featuring native support for the Model Context Protocol and Unity Catalog

From analytics engineer to context engineer

Analytics engineers are evolving into context engineers as the focus shifts from modeling data for dashboards to modeling context for AI agents. The first entry in a new series explores this transition by putting the approach into practice with

Databricks joins the Open Secure AI Alliance to advance AI safety and security

Databricks has joined the Open Secure AI Alliance as an inaugural member to advance open AI safety and secure the entire AI stack alongside partners like NVIDIA. To drive this effort, Dat

Breaking changes include removal of direct AWS and Azure credential fields from model provider configurations and State field type changes in bundle deployment operations. New features add KinesisStreamConfig support for ML streaming sources, a Mode field for pipeline updates, and Pi coding agent attribution in the User-Agent header.

Databricks Completes Acquisition of Panther: Accelerating the Security Lakehouse Era

Databricks has officially completed its acquisition of Panther, combining mature SOC workflows and a software-driven detection engine with Lakewatch’s open security lakehouse foundation. Security teams can now retain

Evaluating and Improving Agent Skills with MLflow

How to build measurable, testable, and continuously improving AI agent capabilities.

Agentic media buying cannot scale without the right foundation. See how buyers and sellers get there on Databricks.

Databricks has released a reference implementation and self-deploy accelerator for agentic media buying, giving buyer and seller organizations a working blueprint where autonomous agents transact using governed data, transactional state, identity, hosted models, and observability unified on one platform. Teams can stand this up directly in their own workspace rather than stitching together a multi-vendor integration, adapting the agents to transact with partners over open standards and shifting effort from manual coordination to strategy and results.

Convert proprietary code to open ANSI SQL with Genie Code

Now in Beta, Genie Code powers an agentic converter that launches swarms of parallel agents to translate proprietary SQL into open ANSI SQL, iteratively validating both syntax and semantic intent. It covers T-SQL, Snowflake, Redshift, Oracle, BigQuery, and Teradata sources, with migration projects in the Databricks workspace to track progress, visualize lineage, and identify objects that need to move together.

Manufacturing runs on capital. Finance protects the margin.

Manufacturing locks an estimated $1.7T in excess working capital across large US companies, trapped in inventory, receivables, and equipment; finance's job is freeing that cash, and AI agents are making that job both faster and more complex. The post argues an ontology is what makes a number trustworthy in business context, and shows how Genie acts as a data-smart finance coworker, surfacing where cash is trapped, where receivables are aging, and which assets underperform with governed, traceable answers.

How agentic AI can help telecom finance teams protect the margin when every moment matters

How preventing revenue leakage became finance's front lineIn telecom, revenue is...

AI customer service: strategy, agents, and solutions guide

AI customer service can reduce operational costs by up to 30% and accelerate resolution times, but navigating hallucination risks and data privacy concerns requires a structured rollout. Successful deployment depends on

AI in supply chain: from demand forecasting to AI agents

Healthcare AI adoption is accelerating fastest in radiology while expanding across diagnostic imaging, clinical documentation, drug discovery, and administrative automation. As regulatory requirements like the European AI Act classify clinical systems as high-risk,

AI Applications in Finance: A Practical Use Case Guide

Finance teams can establish a low-risk path to enterprise scale by adopting a staged rollout featuring two prioritized pilots, a 90–120 day evaluation window, and rigorous ROI measurement. Successful deployment

How the FDA Built an AI Platform That 85% of Its Staff Now Use Daily

The FDA broke down center silos to launch an enterprise AI platform that reached 85% staff adoption in just two months, cutting regulatory research times from days to three minutes. Powered by a governed data

Permission isn't purpose: Intent-based authorization in Omnigent

Omnigent introduces intent-based authorization for AI agents, binding each session to a declared, human-approved purpose rather than relying solely on identity-based permissions. Because any action outside this

Provisioning for the Agentic Era: How Databricks Built a Self-Serve Infrastructure Vending Machine

Polish text:** Databricks built the FE Vending Machine, a self-serve Databricks App that provisions isolated, governed, and use-case-specific cloud resources on demand. By

Simplify AI agent orchestration with Lakebase Postgres

Learn how to turn two Lakebase Postgres tables into a durable, crash-resilient queue for long-running AI agent tasks without operating an external broker, cache, or scheduler. This fully Databr

The last mile: why great first-party data still doesn't make great marketing

Databricks provides a unified data foundation

The three ways AI unlocks transformation in Retail, Travel, and Consumer Goods

Deploying AI as a unified system rather than a collection of pilots allows retail, travel, and consumer goods enterprises to convert unused data into action by eliminating trust, time, and cost barriers. Using the Databricks Platform as a single governed foundation, organizations can secure their competitive moat, scale human capability, and position themselves to capture trillions in projected AI-driven spending.

Building a soccer coaching app on Databricks

Coach's Corner is a Databricks App that processes 25 fps match tracking data into a sub-second 2D/3D tactical bench with replays, event analytics, a scout chat, and an opponent-dossier agent. The end-to-end solution is powered entirely on the Databricks platform, utilizing Lakeflow pipelines for data refinement, DBSQL and Lakebase for rapid querying, and Unity Catalog-governed AI tools like Genie, Vector Search, and MLflow tracing.

The skills gap behind agentic AI — and how Databricks is closing it with a new context engineer certification and agent trainings

Databricks has launched an industry-first Context Engineer Associate certification and expanded its learning catalog with targeted courses to help practitioners build reliable agentic AI systems. The platform is also the first to publish official guidance on utilizing agents like ChatGPT, Genie, and Claude to help candidates effectively study for these certification exams.

Introducing Apache Spark 4.2

Apache Spark 4.2 introduces governed business definitions via metric views, AI-native analytics features like vector retrieval, and simplified real-time data processing through Auto CDC and Real-Time Mode. This release also expands Spark's accessibility from external services and AI agents by leveraging Spark Connect, Arrow-first Python execution, and Python Data Sources.

Inkling model from Thinking Machines Lab now on Databricks

The open-weights Inkling model from Thinking Machines Lab is now available on Databricks through the Unity AI Gateway, allowing practitioners to easily build and deploy AI agents and coding applications on enterprise data. This release enables teams to leverage a model optimized for coding, agentic reasoning, and multimodal inputs while maintaining centralized security, cost controls, and observability.

Data-Native AI Agents: Why Agents Must Move to Your Data

AI agents must run directly within your data stack rather than in a separate, external stack to avoid compounding penalties like fragmented governance, high egress costs, and latency. By deploying data-native agents on the Databricks Data Intelligence Platform, enterprises can leverage an integrated stack of Unity Catalog, AI Search, MLflow, Lakebase, and AI Gateway to ship secure, trusted AI features faster.

How Retail Finance teams are using Agentic AI to protect omni-channel margins

Retail finance teams are increasingly adopting agentic AI and data-smart AI coworkers to turn real-time data into proactive actions that protect margins, cash, and markdown strategies amid rising omni-channel complexity. This shift highlights why ontology and governance have become boardroom priorities for finance leaders looking to drive profitability in the age of agentic commerce.

Foundational context: Cross-industry & function-specific accelerators for Lakebase

Lakebase has launched a suite of partner-led, cross-industry, and function-specific accelerators designed to automate database migrations, power stateful memory for agentic AI, and deliver ready-to-deploy business applications. Databricks practitioners can leverage these solutions to safely rehearse legacy system cut-overs using database branching, maintain real-time context for autonomous agents, and quickly drive business value across finance, marketing, sales, and supply chain operations.

Practical Guide To Python App Hosting

Hosting your Python applications directly next to your lakehouse data eliminates custom integrations, reduces latency, and maintains default security and governance controls. This practical guide explains how to choose the right hosting environment for data-intensive and AI-powered apps, where the hosting and data architecture decisions are inherently linked.

The agentic marketing stack starts with the data layer

Acxiom is building an end-to-end agentic marketing value chain on Databricks, achieving 80 to 90 percent performance improvements by migrating from on-premises data centers to a modern, cloud-native data architecture. This shift allows workflows that once took months to be prototyped in hours, transitioning Acxiom from a traditional data supplier to an embedded intelligence layer inside the marketing stack.