---
title: "AI Agents"
description: "119 posts about AI Agents, summarised, each linking to the original."
---

# AI Agents
> 119 posts about AI Agents, summarised, each linking to the original.

## Articles

### [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.


### [Celebrating the Winners of the 2026 Built-On Databricks Startup Challenge - Cloned](https://yomu.fyi/post/celebrating-the-winners-of-the-2026-built-on-databricks-startup-challe-cloned.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 the Databricks platform, with winners announced at the Data + AI Summit in San Francisco on June 16. VisionHeight won the grand prize for an agentic threat-intelligence platform that maps adversary infrastructure across the Internet while it is being built; Linkup placed second with a production-grade Web Search API delivering results in about two seconds, and Intelo placed third with an agentic workforce for retail merchandising and planning. Clarecast, Gemini Sports, and LakeFusion received honorable mentions for predictive intelligence, football squad planning, and an AI-native data foundation combining MDM, PIM, and LakeGraph. Judges assessed market potential, founding-team caliber, and innovative use of Databricks, while the startup-program offer provides qualifying startups up to $200,000 in credits across Databricks and Neon.


### [Agentic Risk Operations](https://yomu.fyi/post/agentic-risk-operations.md)
- Company: [Ramp](https://yomu.fyi/company/ramp.md)
- Author: Blake Williams, Mueed Rehman, Vishal Sundaram, Brad Hoeweler
- Published: Jun 30, 2026

Ramp describes an architecture for scaling risk operations as payment volume, payment rails, partners, and country coverage expand, rather than letting headcount grow with the business. Agents handle universal intake, gather business context, classify requests, and route work, while machine-learning models trained on millions of historical data points and approved policies make autonomous risk decisions. Operators can modify agent skills and tool configurations without engineering involvement, deploy changes in shadow mode, and rely on asynchronous execution, provider failover, and centralized observability. In payment-risk workflows, structured operator feedback produced a benchmark of more than 1,000 operations covering tool-call trajectories, operator alignment, and downstream outcomes, enabling separate evaluation of agents and policies. Ramp rolls agents out beside operators, then scales operation types and dollar volume using performance thresholds and capped exposure budgets.


### [What To Look For in a Serverless Database for AI Applications](https://yomu.fyi/post/what-to-look-for-in-a-serverless-database-for-ai-applications.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Jun 25, 2026

Serverless databases are presented as a baseline for AI applications whose traffic can be volatile, idle for long periods, or spike when agents fan out queries. The guide distinguishes managed serverless systems from autoscaling products by focusing on architectural separation of compute and storage, true scale-to-zero, cold-start behavior, connection handling, pricing, performance, portability, governance, and AI capabilities such as vector search. It recommends evaluating both low- and high-utilization costs, published warm-up times, tail latency (p95/p99), and built-in pooling or HTTP/Data APIs for high-concurrency agents and serverless functions. The text positions Lakebase as an example that combines serverless Postgres, shared lakehouse storage, and Unity Catalog governance, and cites reported cost and management reductions from a 2025 study while noting that provisioned deployments may suit continuously high-throughput workloads.


### [What if the answer was already in your data?](https://yomu.fyi/post/what-if-the-answer-was-already-in-your-data.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Bryan Smith, Morgan Wilkie, Kaitlin Ryan
- Published: Jun 24, 2026

Kythera Labs is building an AI-native healthcare strategy platform on Databricks to give health systems access to strategic intelligence that historically required specialized analysts or consulting firms. Its foundation converts 339 billion medical and prescription claims covering more than 300 million patients into governed, event-based data, resolving providers, harmonizing codes across 130 vocabularies, and reconstructing patient journeys. Healthcare Strategy Agent, built with Agent Bricks, lets executives ask questions such as where oncology referrals are going and receive analyses of leakage, competing providers, physicians, and reimbursement opportunity in minutes. A Louisiana health system went live within ten days and reported 150% greater visibility into encounters, 12% more keepage, 22% less leakage, and $3.8 million in estimated annualized retained-encounter value. Unity Catalog, Lakebase, Delta Lake, Delta Sharing, and serverless infrastructure provide shared governance, lineage, access controls, and operational integration.


### [Databricks positioned highest in execution and furthest in vision for the second consecutive year in Gartner Magic Quadrant](https://yomu.fyi/post/databricks-positioned-highest-in-execution-and-furthest-in-vision-for.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Craig Wiley, Kasey Uhlenhuth, Kayli Berlin, Cynthya Peranandam
- Published: Jun 24, 2026

Databricks says Gartner positioned it highest for execution and furthest for vision in the Magic Quadrant for the second consecutive year. The post connects this recognition to a category reclassified from “Data Science and Machine Learning” to “AI Platforms for Data Science and Machine Learning,” and argues that agentic applications require enterprise data, governance, observability, and business context. Databricks presents a unified approach combining the lakehouse, Lakebase, Agent Bricks, Unity Catalog, and Unity AI Gateway to build, monitor, and govern agents, models, data, apps, and tools. Reported examples include YipitData’s 20x increase in company coverage with 92–95% tagging accuracy, Block’s unified AI and data estate, and Novo Nordisk’s attribution of more than $157 million in net new value to governed clinical-trial optimization.


### [Guide to Agentic Systems and AI Agents](https://yomu.fyi/post/guide-to-agentic-systems-and-ai-agents.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Jun 23, 2026

Agentic AI systems are goal-directed software platforms that perceive context, plan and execute multi-step workflows, and adapt based on outcomes with minimal human intervention. The guide distinguishes them from traditional and generative AI, defining agents, broader system architecture, and the role of LLMs as reasoning cores connected to memory, APIs, databases, and other tools. It describes a perceive-reason-act-learn loop, multi-step planning, external tool integration through interfaces such as the Model Context Protocol (MCP), and orchestration patterns for coordinating specialized agents. Production concerns include retries, queues, observability, permissions, privacy, logging, and human escalation, while stated risks include reward-hacking, unintended actions, and explainability gaps. It identifies repetitive, data-rich workflows with clear success criteria and bounded error consequences as the best current enterprise candidates.


### [Top 10 AI Business Solutions Driving Company Growth](https://yomu.fyi/post/top-10-ai-business-solutions-driving-company-growth.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Jun 23, 2026

The article identifies ten AI business solution categories presented as growth drivers, while arguing that value is concentrated in workflows where AI changes the economics of work. It frames successful adoption around three conditions: clean, governed data; process-first use-case selection; and governance designed in from the start. Examples include customer-service agents, forecasting, personalization, intelligent process automation, and supply-chain optimization; the text says customer service accounts for 40% of top use cases, while data quality accounts for roughly 75% of what makes an AI solution work. It also describes productivity, automation, and business reimagination as distinct value paths, including a payments-data forecasting product that became an eight- to nine-figure annual revenue stream. The conclusion favors unified platforms that connect data, analytics, AI, governance, and agentic workflows.


### [Data scientists: Powering the future of AI and analytics](https://yomu.fyi/post/data-scientists-powering-the-future-of-ai-and-analytics.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Jun 18, 2026

Data scientists connect statistics, programming, and domain knowledge to turn raw data into models, recommendations, and decisions with business consequences. The role now extends beyond classical modeling to large language models, generative AI applications, agentic systems, and production workflows involving deployment, monitoring, and retraining. Modern practice combines Python, SQL, and libraries such as scikit-learn, PyTorch, Spark, and MLflow with data engineering basics, communication, and domain expertise. Data scientists contribute across problem framing, data access, preparation, feature engineering, experimentation, deployment, and lifecycle monitoring, with governed access and lineage helping align training data with production data. The article concludes that AI assistants and agents can automate routine work, but human judgment remains necessary for trustworthy evaluation, business framing, and actionable recommendations.


### [What is an AI Agent Harness?](https://yomu.fyi/post/what-is-an-ai-agent-harness.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Jun 17, 2026

An AI agent harness is the software infrastructure around a large language model (LLM) that lets it act on tasks rather than only answer prompts. The model reasons and chooses actions, while the harness connects tools, execution environments, memory, external systems and safety controls. Many agents follow a reason–act–observe loop: the model proposes an action, the harness executes it, captures the result and returns that result as context for the next decision. Production harnesses typically include system prompts, tool execution, sandboxes, filesystems and durable storage, context management, feedback and self-verification, guardrails, human-in-the-loop approvals, and observability; their design increasingly shapes reliability as model capabilities converge.


### [Databricks and NVIDIA: Building for the Agentic Era](https://yomu.fyi/post/databricks-and-nvidia-building-for-the-agentic-era.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Hanlin Tang, Tejas Sundaresan
- Published: Jun 17, 2026

Databricks and NVIDIA describe a full-stack partnership for enterprise AI, spanning governed model training, inference, agent infrastructure, and industry-specific AI. Databricks AI Runtime brings NVIDIA Hopper GPUs and Quantum InfiniBand to governed data for distributed training, with Blackwell preparation, Free Edition GPU support, and planned NGC container support. Model Serving uses NVIDIA hardware and Triton Inference Server for low-latency, high-throughput production inference, while NVIDIA Vera targets agent harnesses, tool calls, analytics, and multi-step orchestration with up to 3x faster SQL queries and 80% faster agentic performance. The collaboration also places NVIDIA Agent Toolkit on Databricks Apps, integrates GPU debugging through Genie Code, and brings frameworks such as BioNeMo, Parabricks, cuOpt, and Isaac Sim to Databricks workflows. The stated vision is an end-to-end stack in which GPUs handle models and purpose-built CPUs handle surrounding agentic workloads, with governance supplied by the Databricks platform.


### [The Partner Well-Architected Framework: What's New and What's Next](https://yomu.fyi/post/the-partner-well-architected-framework-what-s-new-and-what-s-next.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: David Porter
- Published: Jun 17, 2026

The Partner Well-Architected Framework (PWAF) has moved partner guidance from static PDFs to AI-ready guidance spanning Built-On, Connected, and Data Collaboration architectures. It combines an architecture center with technical standards, patterns, and instrumentation guidance intended to align integrations with partner-engineering validation while making adoption and DBU impact measurable. Since February, Databricks has added a Dev Kit containing 15+ tested AI-developed skills, new or refreshed guidance for Clean Rooms, software-defined storage, Marketplace apps, Genie, Lakebase, and MCP server onboarding, and an open-source Firefly Analytics reference implementation. The framework is positioned as an evolving, agent-compatible way to shift routine integration work to AI tools, freeing partner and Databricks engineers to focus on complex architecture; more patterns, skills, reference implementations, and demos are planned.


### [What’s coming next to Free Edition](https://yomu.fyi/post/what-s-coming-next-to-free-edition.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Will Valori, Trang Le
- Published: Jun 17, 2026

Databricks is expanding Free Edition, launched twelve months ago to help people learn modern data and AI, after providing more than $10M in credits to over 500,000 learners. The five additions are Genie Code, serverless GPUs subject to availability, Lakebase, Agent Bricks, and Lakeflow Designer. Genie Code can generate, run, interpret, and refine code autonomously for tasks such as dataset analysis, pipeline cleaning, and visualization, while GPUs support deep-learning workloads including training, fine-tuning, inference, and large-dataset processing. Lakebase adds a fully managed Postgres-compatible database for data apps and AI agents; Agent Bricks supplies composable tools, memory, orchestration, and evaluation; and Lakeflow Designer enables visual pipeline construction. Together, these additions are presented as a complete free toolkit covering data engineering, analytics, machine learning, app development, and AI agents, allowing users to build end-to-end data and AI projects in Free Edition.


### [Becoming the most comprehensive data & AI ecosystem on earth](https://yomu.fyi/post/becoming-the-most-comprehensive-data-ai-ecosystem-on-earth.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Stephen Orban
- Published: Jun 17, 2026

Databricks describes a year of expanding its partner ecosystem for independent software vendors and data providers, responding to requests for stronger go-to-market support, technical guidance, and Marketplace transactions. It says it introduced partner tiering, the Partner Well-Architected Framework, a Marketplace Commit Drawdown pilot, and the ability to list Databricks Apps and Genie Agents. Under the pilot, customers with a Universal Commit can submit invoices for eligible solutions that run on or share data to Databricks; validated purchases reduce their commitment obligation, while Databricks compensates its sales team. OpenSharing is presented as an open-source, vendor-agnostic protocol for cross-cloud, cross-platform sharing of structured and unstructured data, volumes, agent skills, AI models, and semantics. The post says Apps adoption has grown fivefold since last year's summit, with more than 5,000 accounts running Apps in production weekly, and that more than 20,000 customers rely on Databricks.


### [What’s new in Genie Code at Data + AI Summit 2026](https://yomu.fyi/post/what-s-new-in-genie-code-at-data-ai-summit-2026.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Julia Powell, Gal Oshri, Weston Hutchins
- Published: Jun 17, 2026

At Data + AI Summit 2026, Databricks announced expansions to Genie Code for complex, agentic data and ML work. The changes include a full-page command center for managing concurrent threads and assets, upgrades across production ML engineering, and scheduled tasks that run prompts while users are away. For ML workflows, Genie Code uses Databricks production expertise and Genie Ontology, integrates with MLflow and Model Serving, and can move GPU jobs to AI Runtime while using workspace environment features. It can write features, coordinate edits, run and debug code, compare candidates, inspect endpoint health, and diagnose issues, with users deciding what to keep. Scheduled tasks are described as coming soon, creating reviewable threads from prompts and optional Databricks assets.


### [Building an open ecosystem for AI governance with Unity AI Gateway](https://yomu.fyi/post/building-an-open-ecosystem-for-ai-governance-with-unity-ai-gateway.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: David Nasi, Kelly Albano, Ashish Kathapurkar
- Published: Jun 17, 2026

Databricks announced the Unity AI Gateway partner ecosystem, extending enterprise AI governance beyond models to runtime interactions among models, agents, MCP servers, skills, and AI tools. Built on Unity Catalog, the gateway lets organizations apply policies, monitor activity, manage spend, and govern AI across providers and frameworks, while integrating security, identity, and governance products they already use. The announcement groups the integrations into runtime AI security, observability and guardrails; agent identity and access governance; and AI observability and risk monitoring. Named integrations include Alice, CrowdStrike Falcon AI Detection and Response, Cyera, HiddenLayer, Netskope, Noma Security, Obsidian Security, Openlayer, Okta, Ping Identity, SailPoint, and Saviynt, with described capabilities including prompt-injection detection, data-loss prevention, agent discovery, authorization, and lifecycle governance.


### [What’s New in the AI Platform: Agents for ML Engineering, Our Deep Learning Platform, and New Capabilities for Real-Time ML](https://yomu.fyi/post/what-s-new-in-the-ai-platform-agents-for-ml-engineering-our-deep-learn.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Tejas Sundaresan, Mike Del Balso
- Published: Jun 17, 2026

The announcement presents three additions to the Databricks AI Platform: Genie Code support for ML engineering, AI Runtime’s serverless GPU environment, and expanded real-time ML capabilities. Genie Code integrates with Unity Catalog, Feature Store, training, serving, monitoring, and MLflow, assisting with feature engineering, model training, deployment, evaluation, and production operations. AI Runtime provides on-demand serverless NVIDIA A10 and H100 GPUs, supports high-performance multinode training with RDMA and high-performance data loading, and adds Lakeflow Jobs, DABs, MLflow, and Unity Catalog integration. For real-time ML, the platform adds declarative feature engineering, streaming features, online feature serving on Lakebase, and enhanced Model Serving targeting 300K+ QPS with under 10ms p99 latency overhead. Reported customer examples include faster workflows, lower infrastructure costs, and production scaling beyond 100K QPS.


### [Introducing the Agentic CDP: A New Species of CDP for a New Era of Agents](https://yomu.fyi/post/introducing-the-agentic-cdp-a-new-species-of-cdp-for-a-new-era-of-agen.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Tasso Argyros, Ali Ghodsi, Reynold Xin
- Published: Jun 17, 2026

Traditional customer data platforms (CDPs) were built for human-managed, batch-based campaigns, but the post argues that agentic buying requires millisecond speed, hyper-personalization, and richer context. It contrasts the familiar Golden Record with Golden Context, which combines customer data with current business goals and the history and outcomes of prior decisions. The proposed Agentic CDP uses “Infinity Campaigns,” always-on engagement loops that use LLMs and agents to adapt messaging, timing, and channels for individuals. It is also embedded in the data foundation, bringing customer, business, and decision context together under existing governance, and is designed for agents and humans from the outset. Databricks presents CustomerLake as an implementation of these principles for its platform.


### [Introducing OpenSharing: the Next Evolution of Delta Sharing for the Agentic Era](https://yomu.fyi/post/introducing-opensharing-the-next-evolution-of-delta-sharing-for-the-ag.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Huey Han, Harish Gaur, Akram Chetibi, Mengxi Chen
- Published: Jun 16, 2026

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.


### [Agent Bricks: Data + AI Summit 2026](https://yomu.fyi/post/agent-bricks-data-ai-summit-2026.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Hanlin Tang, Kasey Uhlenhuth, Akhil Gupta, Patrick Wendell
- Published: Jun 16, 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.


[Newer posts](https://yomu.fyi/topic/ai-agents/page/2.md) · [Older posts](https://yomu.fyi/topic/ai-agents/page/4.md)