AI Agents

How energy teams turn theft detection into governed action with Genie and AI business processes

A Databricks App orchestrates the full lifecycle of energy theft detection—turning ML-flagged suspicious accounts into prioritized investigations, dispatch-ready reports, and tracked recovery workflows, with Lakebase keeping live case state and recovery totals. Genie One, Unity Catalog, Unity Gateway, and Agent Bricks tie this together on one platform, delivering trusted metrics, governed AI usage, and automated executive reporting.

Introducing Consort: Test-driven development on a branching database

Consort brings test-driven development to database branching, giving each branch its own isolated copy of data so tests run against realistic state without touching production. The post walks through what changes when your database can branch like code, demonstrates an agentic development workflow built on it, and shows how to try Consort yourself.

Evaluation-First AI Agents: How Zepto Scales Customer Support on Databricks and MLflow

Zepto built its customer support agents on Databricks and MLflow using an evaluation-first approach, treating traces, golden datasets, and LLM-as-judge checks as core infrastructure and connecting development and production through a dual-loop architecture with a strict quality gate. The result: 65% lower support costs and payback in under a month, giving other AI builders a reusable blueprint for engineering reliability, cost, and risk into production-grade agents.

The 40-year-old database rule agents just broke: How LTAP unifies OLTP and OLAP workloads

LTAP (Lake Transactional/Analytical Processing) resolves the decades-old row-versus-column tradeoff between OLTP and OLAP systems by unifying transactional and analytical workloads at the storage layer rather than the engine layer, succeeding where HTAP has historically stalled. This shift is driven by AI agents, which need near-real-time read and write access to live operational data at a speed and cost neither traditional pipelines nor HTAP architectures can deliver.

Governance beyond security: knowledge, context & ontology on the lakehouse

Existing governance artifacts like classification tags, data contracts, and lineage provide the semantic foundation needed to run catalog-centered AI agent lifecycles directly within Unity Catalog. Anchoring this business context in catalog metadata keeps production PHI within governed boundaries while allowing cheaper models to deliver trusted results.

Announcing the Databricks Big Book of AgentOps

The Databricks Big Book of AgentOps provides a practical blueprint for building, evaluating, governing, and improving AI agents in production. It delivers field-tested architectures, technical best practices, and guidance across observability, cost management, and stakeholder alignment while highlighting common pitfalls to avoid.

How we eliminated $1 million a year of wasted AI agent spend in one hour

Broken MCP tool calls and silent retries can quietly waste over $1 million annually in tokens and engineering hours across AI agent fleets. Tracing tool calls with Unity Gateway and analyzing spend with Genie One enables teams to rapidly deploy fixes and design tools that gracefully handle ambiguous LLM inputs.

How Discovery Bank delivers hyper-personalized banking at scale: behavioral AI, governed data, and real-time decisioning

Discovery Bank achieved a 40% uplift in client engagement impact by building reusable, AI-driven data products and real-time next-best action models on Databricks. Their architecture accelerates data pipeline workflows by 20x and data product creation by 5x while ensuring AI agents operate within existing governance controls.

Building for the AI Era: Lakebase, Streaming, and Lakehouse Innovations at VLDB 2026

Databricks is bringing Lakebase, Structured Streaming, and Lakehouse optimizations to VLDB 2026, spotlighting Lakebase as a third-generation cloud database that decouples transactional compute from storage to support agentic workflows. The company's Engineering and Recruiting teams will also be on-site at the conference.

Vertical Advantage: Transforming Industries with Lakebase and Agentic AI

Databricks Lakebase partners are shipping production-ready, industry-specific solutions—from fraud and regulatory-change agents to real-time claims, prior authorization, dynamic pricing, and grid intelligence—built on a single governed foundation for operational data, analytics, and AI. Powered by Lakebase's serverless Postgres, sub-10ms operational serving, zero-copy branching, and agent-native memory, these solutions are deployable today and designed to turn platform capability into measurable business value.

Object Storage + WAL: Lakebase Postgres for the agentic era

Lakebase Postgres now decouples storage from compute by pairing object storage with a write-ahead log, targeting the I/O bottlenecks agentic workloads create in traditional OLTP setups. The architecture is built for new deployments handling agent-driven database interactions rather than retrofitted onto existing ones.

Introducing Governance Hub: Intelligent, account-level governance over your Databricks estate

Governance Hub gives Databricks admins a single, account-level view of data governance, AI usage, and cost across their entire estate, with each vertical surfacing KPIs and drill-downs so teams can assess governance health without building custom dashboards. A built-in Genie layer adds agentic insights, surfacing anomalies and governance gaps enriched with context about the underlying assets and workloads.

How Databricks Uses AI to Accelerate Incident Investigation

Databricks' AI SRE now handles over 2,000 daily investigations across 150+ teams, helping engineers troubleshoot 100s of microservices spread across 1,500 Kubernetes clusters in 70+ regions and three clouds. Rather than relying on black-box reasoning, the system uses composable agentic runbooks and ties every diagnostic recommendation to verifiable raw evidence, embedding a context-first approach into its design.

Designing effective Genie Agents from a single prompt

Genie Agents let teams turn trusted business context into domain-specific AI agents from a single prompt, reasoning across structured data, documents, and files governed by Unity Catalog without constant prompt tweaking. The post argues that weak agent answers usually stem from missing context rather than bad prompts, and recommends starting with one focused, benchmarked use case as the foundation for expanding with more knowledge, tools, and workflows.

Databricks Document Intelligence: pushing the frontier for complex document extraction

Databricks Document Intelligence's new Precision Mode targets the failure points of complex document extraction—cross-page reconciliation, long outputs like thousands of invoice line items, and schemas requiring reasoning and computation—by pairing custom finetuned extraction models with an agentic harness that reasons in stages and merges parallel subagent outputs. Across six complex document benchmarks, it outperforms the next best frontier model by seven points on accuracy.

Evaluating AI Agents Live at the Grounded Reasoning Cup

Stanford's team won the Grounded Reasoning Cup with 63.3% accuracy on OfficeQA Pro V2, a new 120,000-page U.S. Treasury document benchmark, using an end-to-end agent optimization approach combining reusable skills, document-representation fallbacks, and adaptive verification. Across all 11 academic teams, out-of-the-box frontier agents averaged under 30% accuracy, showing that agent performance tuned on one benchmark doesn't reliably generalize to a new corpus.

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 data primitives like PGlite to AI agent sandboxes. This extends Databricks Postgres capabilities to the edge, allowing lightweight agent databases to sync back to a centralized Lakebase on durable object storage.

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

Omnigent Contextual Policies prevent AI agent data exfiltration by tracking the lethal trifecta—private data access, untrusted content exposure, and outbound transfer—and blocking the outbound step once two legs are active. Configured by humans rather than the model, the policy only triggers when data is actually accessed so ordinary single-leg operations remain unblocked.

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 transforms basic chatbots into action-oriented AI agents by executing a structured loop to interact with external tools, APIs, and systems. Databricks Agent Bricks provides a governed environment to build these agents grounded in enterprise data, featuring native support for the Model Context Protocol and Unity Catalog governance.

From analytics engineer to context engineer

Data modeling is shifting from powering dashboards to structuring context for AI agents, marking the rise of the context engineer. This first installment in a new series examines the transition, starting with how to model internal Gong data.

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 help secure the full AI stack through open research, tooling, and architecture alongside partners like NVIDIA. To advance this effort, Databricks is contributing open technologies including the Omnigent agent meta-harness, the DASF 3.0 security framework, BlackIce red teaming, and the Lakewatch Security Lakehouse architecture for threat detection and response.

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.

Ingest semi-structured data faster and more efficiently with Variant - Now Generally Available

Databricks Variant is now Generally Available, enabling teams to achieve up to 30x faster reads on semi-structured data while handling unpredictable schema changes without pipeline updates. The feature is broadly integrated across the platform, supporting data workloads like Auto Loader and Spark Declarative Pipelines alongside AI tools like Agent Bricks and AI Functions.

Databricks Completes Acquisition of Panther: Accelerating the Security Lakehouse Era

Databricks has officially completed its acquisition of Panther, integrating its software-driven detection engine and SOC workflows with Lakewatch's open security lakehouse foundation. This enables security teams to retain petabytes of telemetry in open formats, deploy autonomous AI agents for real-time triage, and run detections-as-code to accelerate incident response.

Evaluating and Improving Agent Skills with MLflow

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