---
title: "AI Agents"
description: "118 posts about AI Agents, summarised, each linking to the original."
---

# AI Agents
> 118 posts about AI Agents, summarised, each linking to the original.

## Articles

### [Unified context: The missing layer for enterprise AI coworkers](https://yomu.fyi/post/unified-context-the-missing-layer-for-enterprise-ai-coworkers.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Cynthya Peranandam, Christy Maver
- Published: Jul 16, 2026

Enterprise AI assistants often produce fluent answers yet fail to improve forecast calls, deal reviews, and operational standups because decision context is scattered across systems, teams, and competing definitions. Genie One addresses this by using a shared context layer spanning Databricks data, documents, SaaS applications, and operational systems, allowing questions and follow-up work to retain business meaning. Genie Ontology organizes terms, metrics, entities, and relationships into a living knowledge graph, learning from data, dashboards, queries, documents, and connected applications while ranking definitions and signals using usage and certified-asset links. Together with Unity Catalog, it applies permissions, certified data, shared definitions, and governance controls to answers, actions, and agents. The stated outcome is faster movement from decision preparation to action, with less manual reconciliation while preserving accuracy and control.


### [Inkling model from Thinking Machines Lab now on Databricks](https://yomu.fyi/post/inkling-model-from-thinking-machines-lab-now-on-databricks.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Mike Eastham, Yuchen Jin, Preslav Le
- Published: Jul 15, 2026

Databricks announces that Inkling, Thinking Machines Lab’s first open-weights model, is available to enterprise customers through the Unity AI Gateway. The model is positioned for coding and agentic reasoning workflows, supports multi-modal inputs, and can be applied to enterprise data, including proprietary codebases, internal documentation, and domain-specific data. Unity AI Gateway provides centralized security, permissions, audit logging, policy enforcement, cost controls, budgets, and observability, while data remains within the governed environment; Inkling is invoked through a REST API, with SQL query support planned. Teams can try it in AI Playground, deploy a governed endpoint, connect coding agents such as Cursor, OpenCode, or Pi, and build agents with Agent Bricks, with open weights enabling customization and inference-cost optimization without per-token API pricing.


### [AI-Enabled Advisory Services for Higher Education](https://yomu.fyi/post/ai-enabled-advisory-services-for-higher-education.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Chad Ammirati, Zach Langford, Nicole Wong
- Published: Jul 15, 2026

Higher-education call centers face costly, limited-coverage monitoring of advisor conversations and brittle, slow methods for identifying student concerns from transcripts. The proposed workflow deploys OpenAI Whisper on Databricks Model Serving, applies AI Functions for sentiment, topics, intent, and rubric scoring, and uses Unity Catalog to govern the resulting data. For advisor quality, an LLM-as-a-judge evaluates every transcript against a reference-table rubric, returns a weighted 1–5 overall score and per-criterion scores, and routes flagged calls for targeted QA review instead of random sampling. For student insights, quarterly transcript enrichment feeds an Agent Bricks Knowledge Assistant for cited reasoning over raw calls and a Genie Space for structured trend queries, while LangGraph orchestrates UC SQL functions as tools. Together, these components let non-technical advisors, mentors, and QA managers query student interactions without reaching out to a data SME.


### [Data-Native AI Agents: Why Agents Must Move to Your Data](https://yomu.fyi/post/data-native-ai-agents-why-agents-must-move-to-your-data.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Kaan Kuguoglu, John Karlsson
- Published: Jul 15, 2026

Enterprise AI pilots often move data into separate vector databases, SaaS LLMs, or serving layers, creating governance gaps, compounded latency, fragmented costs and observability, and duplicated lifecycle work. The post advocates data-native agents: models, agents, tools, retrieval, and memory run inside the governed data platform, with policy enforced during query planning and computation rather than after responses are produced. It argues that post-hoc controls cannot undo sensitive information encoded in aggregations and can trigger token-burning retry loops. For state and memory, it presents Lakebase, managed PostgreSQL within Databricks, as transactional storage and a shared source of truth for multi-agent swarms. The described platform pattern combines Unity Catalog, Unity AI Gateway, Model Serving, MLflow 3, AI Search, Lakebase, and business-context services, and recommends inventorying workloads already outside the perimeter before closing seams incrementally.


### [Take insights anywhere with Genie One on mobile](https://yomu.fyi/post/take-insights-anywhere-with-genie-one-on-mobile.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Mohit Hingorani, Christine Li, Rhetta Nadas, Sydney Sundell
- Published: Jul 14, 2026

Genie One mobile apps for iOS and Android let business users ask questions of company data, view dashboards, access Databricks Apps, and use conversational agent capabilities away from a desk. Answers draw on Genie Ontology, Genie Agents, enterprise governance, and connectors to Google Drive, Microsoft 365, and Atlassian, while respecting source permissions. AI/BI Dashboards reflow widgets into a single column in portrait mode and preserve their designed layout in landscape. Authentication uses the existing OAuth flow and identity provider, with MFA, conditional access, device posture, network controls, regional data handling, and workspace permissions carried over from the browser; there is no separate mobile backend or mobile-only endpoint. The app is available in Public Preview, with dark mode, push notifications, voice mode, and account-level access listed as planned capabilities.


### [How Retail Finance teams are using Agentic AI to protect omni-channel margins](https://yomu.fyi/post/how-retail-finance-teams-are-using-agentic-ai-to-protect-omni-channel.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Sarah Duffy
- Published: Jul 14, 2026

Omni-channel retail has spread margin, cash, and markdown decisions across more channels, fulfillment paths, and return routes, while agentic systems increase the speed and complexity of change. The post presents ontology as a way to preserve the meaning and business context behind finance figures, keeping definitions, channels, and cost drivers current. Databricks Genie is described as a data-smart AI coworker that answers finance questions in plain language, grounding responses in an evolving ontology, source traces, permissions, and governed AI costs. It focuses on margin after fulfillment and returns, inventory cash tied up in the wrong place, and full-price revenue at risk from markdowns and returns, then prepares actions for a person to approve. Unilever deployed Genie to more than 1,200 finance and business users; analysis that took days now takes minutes, with expected multi-million-euro annual cost avoidance.


### [Foundational context: Cross-industry & function-specific accelerators for Lakebase](https://yomu.fyi/post/foundational-context-cross-industry-function-specific-accelerators-for.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Amit Singh
- Published: Jul 14, 2026

Databricks presents Lakebase as a fully managed, serverless, standard Postgres database for combining operational and analytical workloads on its Data + AI Platform. The platform separates compute from storage, integrates with the lakehouse through Synced Tables and Lakebase CDF, and uses Unity Catalog for governance; copy-on-write branching and autoscaling to zero are described as core infrastructure primitives. The post showcases partner-built, ready-to-deploy accelerators spanning technology, finance, marketing, sales, supply chain, human resources, customer service, and operations. Examples include PostgreSQL migration assessment, multi-agent Genie orchestration, stateful enterprise agents, autonomous data reliability, governed contact-center intelligence, and project operations management. These offerings package Lakebase patterns into migration controls, domain-specific solutions, and agent frameworks intended to accelerate modernization and reduce transformation complexity.


### [Blocking Slow-Burn Attacks: Contextual Policies in Omnigent](https://yomu.fyi/post/blocking-slow-burn-attacks-contextual-policies-in-omnigent.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Nishith Sinha, Matei Zaharia
- Published: Jul 14, 2026

Omnigent’s post examines how a vendor-review assistant can leak confidential pricing terms when an attacker hides an indirect prompt injection in a shared runbook. Because the malicious workflow is divided into ordinary actions, stateless checks approve each step even though the session as a whole is unsafe. The demonstration compares an unprotected run, which sends the summary externally, with a contextual policy that stores a running risk score, adds 30 for each document read, and denies email after the score exceeds 50. It also shows that agents cannot remove or disable policies, new policies require human approval, and any denial prevails when policies are combined. Runtime enforcement therefore preserves the block even when the agent has been misled.


### [The agentic marketing stack starts with the data layer](https://yomu.fyi/post/the-agentic-marketing-stack-starts-with-the-data-layer.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Aly McGue
- Published: Jul 10, 2026

The piece argues that agentic marketing depends on modernizing the data and infrastructure foundation first, rather than starting with isolated AI use cases. At Acxiom, moving core products and client solutions from on-premises Hadoop to Databricks reportedly improved workload runtimes by 80 to 90 percent, reducing jobs that took 50+ hours or as long as 90+ hours to 2–3 hours. The migration also reduced manual pipeline and infrastructure work, freeing engineering capacity for products and client outcomes. Those capabilities now support agentic audience planning, media buying, campaign activation, performance analytics, code generation, testing, and ad variation, with a goal of connecting the full marketing value chain. Because workflows handle PII, generated content passes through legal approval, defined controls, and human oversight; Acxiom is also embedding its data in client platforms and privacy-safe clean rooms to make decisions more transparent and native to customer environments.


### [Ask, build, compose: What our 5th Genie Hackathon taught us about Databricks Genie](https://yomu.fyi/post/ask-build-compose-what-our-5th-genie-hackathon-taught-us-about-databri.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Shruti Prasanna, Rob Bajra
- Published: Jul 9, 2026

The fifth Databricks hackathon used Databricks Genie to test three ways of working with governed data: asking, building, and composing. Genie Agents give business users domain-specific natural-language access to curated data, while Genie Code helps analysts create metric views, functions, pipelines, and dashboards inside Databricks. The composition track uses Conversation APIs and a managed MCP server to make Genie an agent tool alongside custom logic, Model Serving, and other MCP servers, with Unity Catalog governing access. Projects included supervisory routing across 190 tables, governance and analytics products built in days, and multi-agent systems such as ShipBob’s overnight operations brief. Across the tracks, the stated lesson is that shared governance and semantic context let business users, builders, and engineers use Genie at different levels without abandoning grounded, permissioned data access.


### [Benchmarking Coding Agents on Databricks’ Multi-Million Line Codebase](https://yomu.fyi/post/benchmarking-coding-agents-on-databricks-multi-million-line-codebase.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Vinay Gaba, Ankit Mathur, Rishabh Singh, Patrick Wendell, Matei Zaharia
- Published: Jul 8, 2026

Databricks built a coding-agent benchmark from reviewed tasks drawn from its multi-million-line codebase, spanning languages including Scala, Go, Rust, Python, and TypeScript. It uses recent human-written pull requests, held-out tests, standard model and harness setups, and manual validation rather than an LLM judge; Git history was sealed after traces showed agents could recover solutions. Results clustered models and harnesses into three capability tiers and showed that quality, cost per task, and token price can diverge: GLM 5.2 statistically tied Opus 4.8 on quality while costing $1.28 versus $1.94 per task, whereas Sonnet 5 cost $2.09 versus Opus’s $1.94. The same model through different harnesses produced cost differences of more than 2x in some cases while quality remained the same; Pi sent about three times less context per turn. Databricks plans to shift routine work toward cheaper models, deploy open models such as GLM as daily drivers, and improve model-and-harness selection.


### [Contextual Policies in Omnigent: Using session state to better govern AI agents](https://yomu.fyi/post/contextual-policies-in-omnigent-using-session-state-to-better-govern-a.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Matei Zaharia, David Nasi, Xiangrui Meng, Kecheng Cao, Tomu Hirata
- Published: Jul 7, 2026

Omnigent, an open-source meta-harness for AI agents, introduces contextual policies to make agent controls safer and less disruptive than per-action allow, deny, or approval rules. Policies receive session events, maintain private state such as tools used, documents read, accumulated risk, initial intent, and model spend, then allow, deny, transform, or escalate the next action. Omnigent’s server intercepts tool calls from supported agents and applies these policies consistently, while examples include Google Drive restrictions based on documents created or marked confidential, risk thresholds that require approval for later email or file-sharing actions, and budget thresholds that pause or redirect work to a cheaper model. Intent-based authorization limits tools according to the user’s opening request, applying least privilege across supported harnesses and custom agents. The project is described as open source and alpha, with the server providing one interception layer for agents using different harnesses.


### [Reimagining Data Modeling on the Lakehouse: Introducing Vibe Data Modeling](https://yomu.fyi/post/reimagining-data-modeling-on-the-lakehouse-introducing-vibe-data-model.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Amr Ali, Cary Moore, Roberto Bruno Martins, Abhijit Tilak
- Published: Jul 6, 2026

Vibe Data Modeling is presented as a way to build a governed Silver-layer data model from a plain-English description of a business, addressing the months or years often required to hand-build one or adapt a generic industry template. The single-notebook implementation uses four widgets and a multi-model LLM agent to organize organizations into divisions, domains, subdomains, products, and attributes, then generate a logical model and deploy it to Unity Catalog. Its pipeline advances through four generate-and-validate stages, applying 251 rules, deterministic structural gates, two architect reviews, and a retry loop that changes strategy when checks fail. The authoritative model.json also produces schemas, Delta tables, informational foreign keys, classification tags, metric views, an RDFS ontology, DBML, and synthetic sample data, while plain-English refinements create auditable, reversible versions.


### [Scaling Security Alert Triage With Specialized Agents on Databricks](https://yomu.fyi/post/scaling-security-alert-triage-with-specialized-agents-on-databricks.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Leanne Shapton, Connor Hanify, Sam Pezzino
- Published: Jul 6, 2026

Databricks describes a security-alert triage system designed to review low-severity alerts that historically received less attention because of their volume and low fidelity. An initial single-agent prompt escalated 50% of alerts, so the team built a fleet of 17 source-specific agents plus a Threat Intelligence agent, using Structured Streaming to ingest alerts, enrich evidence, and route each alert. The pipeline combines deterministic benign-signal filtering, historical and behavioral context, specialized prompt functions, optional tools, structured dispositions, and cost controls before sending escalations to human analysts. Those analysts provide ground truth through MLflow traces and labels, supporting ongoing evaluation across escalate, monitor, and close decisions. After triaging more than 18,000 alerts, the system reached a 3.2% escalation rate, a 10.5-second median triage time, and more than 6,500 analyst hours saved in 30 days; escalated low-severity alerts were roughly 10 times more likely to be true positives than HIGH and MEDIUM alerts.


### [OpenAI and Databricks at DAIS 2026: Making enterprise AI real](https://yomu.fyi/post/openai-and-databricks-at-dais-2026-making-enterprise-ai-real.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Margaret Amori
- Published: Jul 6, 2026

At Data + AI Summit 2026, Databricks and OpenAI presented a partnership centered on combining OpenAI’s frontier models and agents with Databricks’ enterprise context and control. The post describes GPT models and Codex running natively on Databricks, with Unity AI Gateway governing interactions, budgets, routing, auditing, and visibility, while Agent Tools provide governed enterprise data access through MCPs. Examples include OpenAI’s marketing data foundation on Databricks, which cut storage costs by $400,000 per month, and a Hertz application built in 11 business days with GPT-5.5 and Databricks that lifted lead conversion from 60–65% to 75–80%. The sessions argue that deployment, security, evaluation, monitoring, context, and sharing comprise most of the work around enterprise agents. A joint virtual event scheduled for August will address shipping agentic applications at scale, including a Stellantis case study.


### [The 3 questions to answer to take AI from experimentation to impact](https://yomu.fyi/post/the-3-questions-to-answer-to-take-ai-from-experimentation-to-impact.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Christy Maver
- Published: Jul 2, 2026

The post argues that enterprises moving AI from experimentation to impact should answer three questions: whether employees and governance are ready, whether tools are accessible, and whether workers have the capabilities to use them. It recommends secure, governed AI agents that let employees experiment safely, apply consistent oversight across workloads, and build skills without compromising business security; the text notes that fewer than half of companies have formal governance for autonomous workloads. AI should appear inside natural workflows, including single chat interfaces and embedded intelligence dashboards, with access to company data, automated identity management, consistent governance, and business logic across engagements. Finally, agents should provide contextually accurate, actionable intelligence and automation, challenge users’ thinking, suggest next steps, and take action rather than only answer questions.


### [Inside the infrastructure strategies propelling AI leaders](https://yomu.fyi/post/inside-the-infrastructure-strategies-propelling-ai-leaders.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Christy Maver
- Published: Jul 2, 2026

The post examines why enterprise AI efforts can become too expensive, slow, and difficult to scale, citing survey findings that 67% of organizations with disconnected data environments identify storage, movement, and duplication as their largest recurring AI cost. It presents three infrastructure considerations: delivering infrastructure at agentic speeds, streamlining data, and adopting systems built for AI scale. Examples include instant temporary environments with secure rollback and restoration, unified operational and analytical data stored separately from compute in low-cost cloud storage, and elastic scaling that can move from high concurrency to zero in seconds. The conclusion is that open, AI-ready, purpose-built databases can reduce pipeline complexity and costs, support experimentation, and let organizations align spending with unpredictable workloads while enabling faster AI innovation.


### [Celebrating the Winners of the 2026 Built-On Databricks Startup Challenge](https://yomu.fyi/post/celebrating-the-winners-of-the-2026-built-on-databricks-startup-challe.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Andrew Ferguson, Joslyn O'Connell, Sully Clark
- Published: Jul 1, 2026

The 2026 Built-On Databricks Startup Challenge recognized early-stage startups building B2B applications on Databricks, with winners presented at the 2026 Data + AI Summit. VisionHeight won the Grand Prize for an agentic threat-intelligence platform that maps adversary infrastructure across the Internet while it is being constructed, aiming to give defenders earlier warning. Linkup took second place with a production-grade Web Search API that independently crawls and indexes the open web at the fact level, delivering sourced results in about two seconds. Intelo placed third with five coordinated AI Agent Teams covering retail merchandising and planning, while Clarecast, Gemini Sports, and LakeFusion received Honorable Mentions. Judges assessed market potential, founding-team caliber, and innovative Databricks use; the announced Startup Program also offers qualifying startups up to $200,000 in combined Databricks and Neon credits.


### [Beyond dashboards: Introducing Decision Execution Platforms](https://yomu.fyi/post/beyond-dashboards-introducing-decision-execution-platforms.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Marc Solomon, Marcello Pedersen
- Published: Jul 1, 2026

Databricks Forward Deployed Engineering introduces Decision Execution Platforms (DEPs), an enterprise analytics category intended to connect KPI signals, executive decisions, operational execution, and measured outcomes. The proposal addresses workflows in which dashboards reveal problems but meetings, decks, spreadsheets, and messaging threads leave implementation fragmented and impact measurement disconnected. DEPs run the four-stage loop on governed Databricks infrastructure: agents recommend actions, alternatives, predicted impact, and reasoning; approved choices execute through systems of record; and results persist in a Decision Log for continuous learning. Their architecture combines a foundation of Lakebase, Genie, Unity Catalog, Lakehouse, Agent Bricks, and MLflow with an SDK of reusable primitives and a Databricks Apps executive surface. A retailer case used a DEP to unify fulfillment data and enable simulated, controlled rerouting, with scaling aimed at measurable bottom-line and customer-satisfaction outcomes.


### [Forecasting at the speed of modern retail](https://yomu.fyi/post/forecasting-at-the-speed-of-modern-retail.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Ryuta Yoshimatsu, Puneet Jain, Lourdes Angélica Martinez Medina, Lucas Bruand, Dael Williamson
- Published: Jul 1, 2026

Retail and CPG forecasting now spans hundreds of thousands, sometimes far more, time series across fragmented channels, promotions, and short-lived products, making legacy methods and manual exception management difficult. Multi-model forecasting addresses this complexity by evaluating a range of techniques against actual data and selecting the best-performing model for each series, but enterprise-scale experiments require scarce forecasting and distributed-systems expertise. Released in 2024, Databricks’ open-source Many Model Forecasting (MMF) integrates more than 35 statistical, deep-learning, and foundation time-series models and runs on distributed Databricks compute. MMF Agent, built on Genie Code, guides users through data quality, series classification, compute configuration, forecasting, post-processing, and model selection, while Unity Catalog helps it use organizational data context. The workflow is intended to reduce setup from days to hours, improve targeting and accuracy, and make rigorous forecasting more accessible while remaining customizable for technical teams.


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