---
title: "AI Agents"
description: "574 posts about AI Agents, summarised, each linking to the original."
---

# AI Agents
> 574 posts about AI Agents, summarised, each linking to the original.

## Articles

### [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
- Type: Benchmark

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
- Type: Explainer

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.


### [Notion HQ – Tools & craft – Notion](https://yomu.fyi/post/notion-hq-tools-craft-notion.md)
- Company: [Notion](https://yomu.fyi/company/notion.md)
- Published: Jul 7, 2026
- Type: Announcement

This Notion HQ page collects product announcements, company updates, and stories about extending Notion’s workspace. It introduces a Developer Platform, Custom Agents that handle entire workflows autonomously, Notion 3.0 with Agents, Notion Calendar, and Notion AI for Work, described as an all-in-one AI toolkit built into the workspace. The updates also cover connectivity and data consolidation, including the acquisition of Automate.io, which built integrations for more than 200 software tools, and the welcome of Sequin, whose tools brought data from across software stacks together. Additional announcements report ZeroEntropy joining Notion, a new board of directors, a security update dated July ’26, and a private tender offer involving GIC, Sequoia Capital, and Index Ventures. The page also includes Custom Agents beta learnings, conference highlights, and a profile of a Notion consultancy.


### [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
- Type: Announcement

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.


### [Government of Alberta uses Claude to find and fix cybersecurity vulnerabilities](https://yomu.fyi/post/government-of-alberta-uses-claude-to-find-and-fix-cybersecurity-vulner.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Jul 6, 2026
- Type: Explainer

The Government of Alberta’s Ministry of Technology and Innovation has used Claude Code with Claude Opus and Sonnet since 2025 to review, secure, and modernize systems spanning 27 provincial ministries. About 50 autonomous agents scanned 466 million lines across 3,400 repositories in roughly 20 hours, using a rules-engine first pass followed by reviews that cite exact files and lines; Alberta estimates a traditional review could take around 6.5 years. Claude Code also generated and tested fixes, wrote missing tests, and rebuilt some obsolete applications, with Ministry engineers approving patches before release. Specialized red-team and blue-team agents now run continuous checks against roughly 95 security controls, while the Alberta AI Academy trains employees and members of the public. The government plans to consolidate 185 legacy applications into 16 reusable modern applications, a future goal rather than a reported result.


### [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
- Type: Problem & solution

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
- Type: Announcement

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.


### [How to Build and List Secure Cross App Access (XAA) Connections on Okta Integration Network (OIN)](https://yomu.fyi/post/how-to-build-and-list-secure-cross-app-access-xaa-connections-on-okta.md)
- Company: [Okta](https://yomu.fyi/company/okta.md)
- Author: Gagan Sikri
- Published: Jul 6, 2026
- Type: Tutorial

Cross App Access (XAA) provides an identity framework for securing token-based communication when software or AI agents access external ecosystems through trusted identity providers. It replaces long-lived API keys and hardcoded secrets with real-time identity propagation, while allowing enterprises to control connected apps, requested scopes, and user access. The guide explains prerequisites for requesting and resource apps, including OIDC or SAML SSO, authorization-server support, protected resources, scopes, an Okta Integrator Free Plan org, and tested workflows. It then describes token exchange testing: requesting apps obtain an ID token and mint an ID-JAG, while resource apps validate the ID-JAG and issue a scoped access token. To seek OIN listing and XAA enablement, applicants submit SSO integration details, metadata, testing evidence, and a questionnaire to Okta.


### [Claude's extended thinking](https://yomu.fyi/post/claude-s-extended-thinking.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Jul 3, 2026
- Type: Explainer

Claude 3.7 Sonnet introduces extended thinking, letting users toggle deeper reasoning and developers set a thinking budget. The same model spends additional computation on sequential reasoning, while agent training supports iterative function calls, computer use, and open-ended tasks. The post examines visible thought processes, noting benefits for trust and research but also limitations around faithfulness, safety, and jailbreak risk; it therefore treats the feature as a research preview. Reported examples include stronger OSWorld performance, playing Pokémon Red through tens of thousands of interactions and progressing through three Gym Leaders, plus logarithmic math gains as thinking tokens increase. Safety work includes encrypted thoughts for some harmful content and computer-use prompt-injection mitigations that raise prevention from 74% to 88%.


### [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
- Type: Explainer

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
- Type: Explainer

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
- Type: Announcement

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
- Type: Explainer

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
- Type: Announcement

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
- Type: Announcement

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.


### [Build agentic full-stack apps with Genkit](https://yomu.fyi/post/build-agentic-full-stack-apps-with-genkit.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Chris Gill
- Published: Jul 1, 2026
- Type: Announcement

Genkit Agents is an open-source, full-stack foundation for conversational AI and agentic applications, supporting TypeScript, Go, Dart, and Python. The Agents API packages message history, tool loops, streaming, persistence, and frontend communication behind a shared interface: agents can run in process or behind HTTP, and clients use chat() through remoteAgent(). Developers can choose client-managed state or session-store-backed snapshots, resume by session ID, branch from snapshot ID, stream text, custom state, and artifacts, and pause tools for human approval. The framework also supports detached long-running work, specialist delegation, persistence choices including in-memory, file, Firestore, and custom stores, plus an Agent Runner in the Developer UI. The Agents API is in preview for TypeScript and Go, with breaking changes possible in minor releases.


### [Why we built ADK 2.0](https://yomu.fyi/post/why-we-built-adk-2-0.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Swapnil Agarwal, Alan Blount, Frank Guan
- Published: Jul 1, 2026
- Type: Explainer

ADK 2.0 addresses the structural challenge of moving AI agents from prototypes into production, where infinite loops, hallucinated bypasses, and failures without clean exceptions make model-led orchestration unreliable. Because language models are slow, costly, and variable for routing, scheduling, and error handling, the release separates execution routing from language processing and reserves LLMs for ambiguous or cognitive steps. Building on ADK v1’s support for Python, Java, Go, TypeScript, and Kotlin, it adds a structured workflow runtime and task-collaboration model, with Workflows available in Python since March and newly launched for Go. Deterministic graphs combine tool calls, Human-in-the-Loop steps, and specialized agents with explicit branching and error handling. Dynamic workflows extend this model with native Python and asyncio control flows for retries, branching, and reusable sub-workflows.


### [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
- Type: Problem & solution

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.


### [Driving the Agent Quality Flywheel from Your Coding Agent](https://yomu.fyi/post/driving-the-agent-quality-flywheel-from-your-coding-agent.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Dima Melnyk, Jason Dai
- Published: Jun 30, 2026
- Type: Tutorial

The quality-flywheel skill addresses the gap between prompt tweaks that improve a few examples and evidence that an agent improved without regressions. A coding agent orchestrates dataset preparation, inference, trace grading, failure analysis, and optimization, using Google's adaptive AutoRaters or custom metrics while keeping the optimizer and evaluator independent. In a travel-concierge example, the revision\_honored rubric found that 21% of cases ignored mid-conversation trip changes, exceeding the stated action threshold. A separate software-bug-assistant cycle raised tool-disclosure responses from 0% to 96% across 15 cases, while production traces and online monitors extend the same loop beyond synthetic scenarios. Human approval remains required, and adaptive scores are treated as directional signals rather than absolute grades.


### [The Builder Revolution: Why We're Shifting to Builder Advocacy](https://yomu.fyi/post/the-builder-revolution-why-we-re-shifting-to-builder-advocacy.md)
- Company: [Okta](https://yomu.fyi/company/okta.md)
- Author: Michael Palermo
- Published: Jun 30, 2026
- Type: Announcement

The team formerly known as Developer Advocacy, within Developer Success, has been renamed Builder Advocacy within Builder Experience (BX). The change responds to an expanded definition of who builds with technology: alongside traditional developers, AI builders and no-code or low-code automators are creating production-level experiences and mission-critical infrastructure. The organization says its advocacy will serve developers, prompt engineers, LLM integrators, AI-agent creators, ML practitioners, and automation-focused builders, while continuing support for traditional coders, engineers, and architects. The announcement cites GitHub’s 180 million developers, 36 million new developers in 2025, and Stack Overflow’s finding that 84% use or plan to use AI tools. The rename is effective immediately and begins broader investments in resources, content, and community programs for this wider builder community.


[Newer posts](https://yomu.fyi/topic/ai-agents/page/16.md) · [Older posts](https://yomu.fyi/topic/ai-agents/page/18.md)