---
title: "AI Agents"
description: "571 posts about AI Agents, summarised, each linking to the original."
---

# AI Agents
> 571 posts about AI Agents, summarised, each linking to the original.

## Articles

### [Helix: The internal tool powering our Shopify app's native migration (2026) - Shopify](https://yomu.fyi/post/helix-the-internal-tool-powering-our-shopify-app-s-native-migration-20.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: Talha Naqvi
- Published: Sep 21, 2026
- Type: Announcement

Shopify’s internal tool Helix powers the native migration of its Shopify app, which has over 300 screens. The company previously rebuilt the Shop app in just 12 weeks using similar methods. Helix is a set of tools and skills that help LLMs migrate features and screens from the React Native app while following a highly opinionated architecture. Unlike most tools that gather information, create specs, and implement everything at once, Helix aims for consistent, high-quality, and maintainable results. The approach is now being applied to the larger Shopify App after its success with Shop.


### [Building Secure AI Agents with Microsoft Agent Framework and Auth0: Human-in-the-Loop Approval](https://yomu.fyi/post/building-secure-ai-agents-with-microsoft-agent-framework-and-auth0-hum.md)
- Company: [Auth0](https://yomu.fyi/company/auth0.md)
- Author: Andrea Chiarelli
- Published: Sep 21, 2026
- Type: Tutorial

The post details how to add secure human-in-the-loop approval to an AI expense agent built with Microsoft Agent Framework and Auth0, completing the four-part series that handles identity, authorization, and action. Because approval is a security-sensitive action requiring the same cryptographic rigor as any authentication event, the agent uses Client-Initiated Backchannel Authentication (CIBA) to push a notification to the manager’s phone instead of accepting chat input. The notification includes a binding message describing the expense by ID, amount, vendor, and context, giving the manager enough information to decide from a lock screen. The agent polls Auth0’s token endpoint until the manager approves or denies, then uses the CIBA-issued access token scoped with approve:expenses to record the decision on a separate Expense API. This design ensures approval decisions are cryptographically bound to the manager’s identity and verifiable by downstream services, completing the secure loop.


### [The AIDEs Framework: How We Built a “Theory of Everything” for AI Development Tools](https://yomu.fyi/post/the-aides-framework-how-we-built-a-theory-of-everything-for-ai-develop.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Stanislav Tikhomirov
- Published: Sep 18, 2026
- Type: Explainer

JetBrains presents the AIDEs Framework as a model for understanding how AI development tools evolve, rather than trying to track every market change. The post describes a progression from single-line completion through multiline completion, IDE chat, AI code editors, agentic development environments, and cloud-based agents. It defines an Artificial Intelligent System as a system that demonstrates intelligence in helping users achieve goals, and an Artificial Intelligent Development Environment (AIDE) as an AIS for creating software; intelligence may come from deterministic heuristics as well as LLMs. The framework treats principals and agents as potentially human or AI actors and views software creation as a broader delegation process than coding alone. It proposes examining assumptions, building a model, and validating it against ongoing change, while suggesting a possible future of vertically integrated Agentic Software Production Platforms that use AI as an “intelligence” resource.


### [Making Local AI Smarter and Faster](https://yomu.fyi/post/making-local-ai-smarter-and-faster.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Vladimir Sotnikov
- Published: Sep 18, 2026
- Type: Announcement

Junie Local’s update introduces Qwen3.8-3.6-27B-blend, a merged model that balances speed and capability for local coding agents. The blend, created by merging Qwen3.6 and Qwen3.8 weights in equal proportions, completed 37 tasks in a 100-task internal benchmark, compared to 34 for Qwen3.6 and 39 for Qwen3.8, while generating 71% fewer output tokens than Qwen3.8. The runtime uses multi-token prediction (MTP), with a two-token proposal on M5 MacBook Pro making decoding 60% faster. A four-bit MTP head was kept over an eight-bit one due to similar acceptance rates and memory savings. The update also brings experimental NVIDIA RTX support on Windows for machines with at least 24 GB of VRAM.


### [Database for AI Agents: 5 Evaluation Criteria](https://yomu.fyi/post/database-for-ai-agents-5-evaluation-criteria.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Sep 18, 2026
- Type: Explainer

The post defines a database for AI agents as infrastructure for short-term, episodic, procedural, and operational memory, rather than merely a place to retrieve documents. It proposes five evaluation criteria for moving agent workloads from prototypes to production: per-agent branch isolation, serverless scale-to-zero, hybrid search, ACID guarantees, and unified platform access. The technical rationale is that agents perform repeated reads and writes, resume tasks, share state concurrently, and need current data across vector, keyword, and metadata retrieval; zero-copy branches, sub-second wakeups, and single-query hybrid search address specific operational constraints. It concludes that a database missing more than one minimum bar becomes a production risk, while standard Postgres alone supplies ACID but not branching, scale-to-zero, or unified operational and analytical access.


### [How Databricks Helps Baseball Teams Gain an Edge with Data & AI](https://yomu.fyi/post/how-databricks-helps-baseball-teams-gain-an-edge-with-data-ai.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Nick Ragonese, Wesley Pasfield
- Published: Sep 18, 2026
- Type: Explainer

Baseball teams can use Databricks to turn Statcast and team-derived pitch-level data into game-day plans for hitters, bullpen changes, pinch-hit choices, advance scouting, and roster decisions. Genie, on Unity Catalog, queries governed Delta tables and reusable Unity Catalog SQL functions for tendencies by count, handedness, pitch type, location, and base-runner state. A Multi-Agent Supervisor built with Agent Bricks and deployed on Model Serving simulates matchup pockets, compares reliever arsenals with hitter profiles, and produces explanations that staff distill into printed decision cards. For advance work, AI Search indexes pitcher and hitter embeddings to find similar arms, while Lakebase Postgres keeps reports, tags, and decisions close to the source data for shared operational memory.


### [What is Agentic Analytics?](https://yomu.fyi/post/what-is-agentic-analytics.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Sep 18, 2026
- Type: Explainer

Agentic analytics is an approach in which autonomous AI agents continuously monitor data, detect patterns and anomalies, generate contextual insights, and take actions with minimal human intervention. Unlike traditional business intelligence, which relies on people to query dashboards and interpret reports, these systems operate as goal-directed decision loops that can recommend or trigger alerts, forecasts, workflows, and system changes within defined boundaries. Their architecture combines data access, agent orchestration, LLM-based reasoning, a semantic layer, action integrations, and feedback signals. The source identifies faster insight, lower reporting overhead, reduced analytics backlogs, and potentially better forecasting as benefits, while stressing governance, auditability, security, monitoring, and human oversight to control errors and unintended outcomes.


### [Memory scaling for AI agents](https://yomu.fyi/post/memory-scaling-for-ai-agents.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: The Databricks AI Research Team
- Published: Sep 18, 2026
- Type: Explainer

Memory scaling is proposed as an agent’s performance improving when it accumulates past conversations, feedback, interaction trajectories, and business context in persistent external memory. Unlike model-parameter or inference-time scaling, it relies on selective retrieval to supply relevant information without filling long context windows. In MemAlign experiments on Databricks Genie Spaces, adding labeled examples raised test scores from near zero to 70%, about 5% above an expert-curated baseline, while average reasoning steps fell from about 20 to 5; filtered user logs produced similarly strong gains, exceeding 50% after 62 records. Production use requires storage and retrieval for episodic and semantic memories with organizational and user-level scoping, plus controls that preserve permissions and sensitivity labels.


### [Auth0 Agent Gateway: The Identity Control Plane for Customer-Facing AI](https://yomu.fyi/post/auth0-agent-gateway-the-identity-control-plane-for-customer-facing-ai.md)
- Company: [Auth0](https://yomu.fyi/company/auth0.md)
- Author: Andrés Aguiar
- Published: Sep 18, 2026
- Type: Announcement

Auth0 Agent Gateway is announced in beta as a Customer Identity and Access Management (CIAM) control plane for governing product-native, customer-facing AI agents in multi-tenant SaaS applications. It addresses a trust model involving organizations, delegated user access, customer consent, independently owned connected accounts, and cross-tenant data boundaries, rather than workforce-only identity. Positioned between an agent and its tools, the gateway evaluates each call using agent, user, and organization context; it uses Organizations, Agents as Principals, Token Vault, token exchange, tenant logs, and administrative controls. The described capabilities include short-lived agent-only tokens, user-scoped credentials, runtime tool authorization, audit logging with optional SIEM streaming, and an agent kill switch. Auth0 is seeking product and engineering teams to join the beta.


### [Move and Scale Faster with FGA Permissions Index](https://yomu.fyi/post/move-and-scale-faster-with-fga-permissions-index.md)
- Company: [Auth0](https://yomu.fyi/company/auth0.md)
- Author: AI Is Reshaping Authorization in the B2B SaaS Landscape
- Published: Sep 18, 2026
- Type: Announcement

Auth0 FGA Permissions Index is announced in Early Access as a way for B2B SaaS applications to enforce permissions while handling expanding AI-agent activity and complex relationship graphs. Customers select relations used for search and filtering, typically high-cardinality relations such as can\_view, and the index precomputes the access those relations imply. It streams the resulting data into the customer’s database alongside application data, allowing indexed permission checks through SQL JOINs instead of repeated authorization API calls or runtime graph traversal. Computation moves to relationship-write time, while relations outside the index continue to be evaluated by FGA in real time. The announcement positions this design as a path to consistently low-latency filtering at enterprise scale, with permission decisions described as precomputed, traceable, and compliant.


### [Databricks Announces Lakewatch: New, Agentic SIEM](https://yomu.fyi/post/databricks-announces-lakewatch-new-agentic-siem.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Reynold Xin, Andrew Krioukov, Molly Limaye, Taylor Kain, Keegan Dubbs
- Published: Sep 18, 2026
- Type: Announcement

Databricks announces Lakewatch, an agentic SIEM intended to help organizations defend against increasingly sophisticated, AI-driven attackers. Launching in Private Preview, it unifies security, IT, and business data in a governed lakehouse built on Unity Catalog, while supporting open OCSF formats and storage in customer-controlled cloud object stores. Lakeflow Connect ingests and normalizes major security sources, and Detection-as-Code lets teams define YAML rules with SQL queries or Python notebooks, backtest them against historical data, and deploy through CI/CD. Genie Code and Genie Spaces provide AI-assisted ingestion, detection authoring, rule refinement, investigation, and natural-language threat hunting, while custom models can support anomaly detection, behavioral analytics, and entity risk scoring. The announcement also introduces an Open Security Lakehouse Ecosystem and acquisitions of Antimatter and SiftD.ai; Lakewatch is available in Private Preview, with Adobe and Dropbox among the named customers.


### [Announcing General Availability and Open Sourcing of Unity Catalog Business Semantics](https://yomu.fyi/post/announcing-general-availability-and-open-sourcing-of-unity-catalog-bus.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Can Efeoglu, Sachin Thakur, Richard Tomlinson, Justin Talbot, Daniel Rosenthal, Jasmeet Jaggi
- Published: Sep 18, 2026
- Type: Announcement

Databricks announces the general availability of Unity Catalog Business Semantics, a unified semantic foundation for consistent business definitions across BI dashboards, notebooks, SQL applications, and AI agents. Built into Unity Catalog, it applies existing governance, security, lineage, and access controls to centrally defined Metric Views, whose metadata includes display names, formats, and synonyms. Metric Views are authored declaratively in SQL, compiled and executed deterministically at query time, and can use materializations with automatic pre-aggregation, incremental refresh, intelligent query rewriting, and transparent routing; materialization remains in Preview. The core Metric View implementation is being open sourced in Apache Spark OSS for an upcoming release, with Unity Catalog OSS v0.5 support coming soon, while a point-and-click authoring UI is in Public Preview.


### [How Addepar scales investment workflows with Databricks AI agents](https://yomu.fyi/post/how-addepar-scales-investment-workflows-with-databricks-ai-agents.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Bob Pisani
- Published: Sep 18, 2026
- Type: Explainer

Addepar, a global technology and data platform, manages nearly $9 trillion in assets and moved from older systems to a unified data intelligence platform on Databricks running on AWS. It ingests hundreds of disparate data feeds, standardizes and enriches them, and delivers results through products, APIs and data sharing. Since adoption, pipeline costs fell 60% versus legacy infrastructure, saving more than $2 million, while new pipelines and integrations became five times faster. Addison, a native AI experience embedded in the platform, combines governed portfolio Q&A, proactive insights and action-oriented workflows, using live data and Addepar’s core calculations engine. Addepar is adopting Databricks Agent Bricks, including Supervisor Agent flows with Genie-powered analytics, while Foundation Model Serving and managed MLflow support governed model serving, traceability, evaluation and iteration for human-supervised agentic workflows.


### [AiChemy: Next](https://yomu.fyi/post/aichemy-next.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Yen Low, Sean Zhang
- Published: Sep 18, 2026
- Type: Tutorial

AiChemy is a multi-agent assistant for cross-disciplinary research that combines external MCP servers including OpenTargets, PubChem, and PubMed with Databricks-managed chemical libraries. It can identify disease targets and drug candidates, retrieve chemical and pharmacokinetic properties, assess safety and toxicity, and attach findings to verifiable data sources. Its guided workflow covers disease-to-target-to-drug discovery and literature validation, while a vector search over a 250,000-molecule ZINC15 index uses 1024-bit ECFP embeddings to find compounds similar to Elacestrant. To build a custom supervisor, users connect public MCP servers, transform structured drug data into a Genie space, and index unstructured chemical data for similarity search. They can assemble it with no-code Agent Bricks or deploy a Langgraph supervisor with Lakebase, MLflow AgentServer, and a React interface; invocations are logged and traced through MLflow using OpenTelemetry standards.


### [How FSIs eliminate silos between clients, operations, and finance](https://yomu.fyi/post/how-fsis-eliminate-silos-between-clients-operations-and-finance.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Anindita Mahapatra, Emily Piekarski, Shirly Wang
- Published: Sep 18, 2026
- Type: Problem & solution

Financial institutions often split portfolio, operations, actuarial, and finance work across separate systems, even when teams depend on the same portfolios, positions, liabilities, and transactions. Databricks presents a unified, governed platform combining Databricks SQL, Unity Catalog Metric Views, Lakebase, Genie, Databricks Apps, and Lakeflow Designer to connect analysis with operational action. A worked workflow follows an actuary's duration analysis, a portfolio manager's scenario modeling and mandate changes, operations' IBOR/ABOR reconciliation, and finance's approval of adjustments and ledger entries. Governance features including row-level security, column masking, RBAC, ABAC, and shared business semantics keep access and terminology consistent, while Lakebase records corrections in auditable, timestamped tables. The result described is a closed-loop process in which strategy, risk, and accounting use the same governed data rather than reconciling separate systems.


### [A skills library for every agent](https://yomu.fyi/post/a-skills-library-for-every-agent.md)
- Company: [Notion](https://yomu.fyi/company/notion.md)
- Author: Geoffrey Litt
- Published: Sep 17, 2026
- Type: Announcement

The post introduces Notion’s skills library and Skills API, aimed at helping teams share organizational knowledge across AI agents and workflows. The library is designed for nontechnical and technical users, with collaborative editing, database organization, supporting files, permissions, suggested edits, version history, usage analytics, and controls over skill distribution. The API provides endpoints for loading skills in spec-compliant file formats, with examples including synchronization to GitHub, installation through Vercel’s Skills CLI, and uploads or edits through MCP tools. Notion says early customers and partners are already using the API across agents and tools, while an open-source starter kit supports Notion-to-GitHub synchronization. Vercel’s Skills CLI can install individual Notion skill pages or workspace plugins, using standards-compliant files without handling Notion-specific formats.


### [How DHI Group accelerates generative AI workloads from idea to production using hackathons](https://yomu.fyi/post/how-dhi-group-accelerates-generative-ai-workloads-from-idea-to-product.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Umesh Kalaspurkar
- Published: Sep 17, 2026
- Type: Explainer

DHI Group needed to move generative AI efforts beyond proofs of concept because its traditional software development lifecycle took months, while it sought faster delivery, technical validation, and broader AI literacy. AWS and DHI used a four-phase Hackathon Acceleration Package—preparation, enablement, a three-day build event, and a path to production—with workshops on Amazon Bedrock AgentCore, AI-DLC, and Kiro. Twenty participants formed three teams and produced prototypes for employer analytics, intelligent candidate matching, and a unified ClearanceJobs–AgileATS marketplace; DHI leadership committed to advancing all three toward production. The winning marketplace uses an MCP server, Amazon Bedrock AgentCore, Lambda functions, session memory, and Claude 3.5 Haiku to let recruiters search candidates and enrich profiles through natural-language requests. The post concludes that time-boxed, cross-functional hackathons can compress ideation, architecture, prototyping, executive alignment, and production planning when production criteria and preparation are established upfront.


### [Why client SDK generation belongs in the open](https://yomu.fyi/post/why-client-sdk-generation-belongs-in-the-open.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Amir Hardon, Philipp Schmid
- Published: Sep 17, 2026
- Type: Announcement

Google describes partnering with Speakeasy to open-source the OpenAPI code-generation suite used for new SDKs for its Interactions, Agents, and Webhooks APIs. The change follows the May 2026 shutdown of a proprietary SDK-generation provider, which exposed platform risk while Google was preparing the Interactions API for general availability; migration prioritized avoiding breaking changes and preserving type definitions, strict error hierarchies, and streaming behavior. The AGPLv3 suite generates statically typed libraries for Python, TypeScript, Go, Java, C#, PHP, and Ruby, plus standalone agent-native CLI binaries and documentation MCP servers built from OpenAPI specifications and Markdown. Google says a deterministic generator paired with Antigravity AI agents now supports its pipeline across six targets, including three released SDKs, with roughly one engineer maintaining it, while generated code can remain under a chosen license such as MIT or Apache 2.0.


### [A multi agent approach audience intelligence](https://yomu.fyi/post/a-multi.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Bradley Munday, Tyler Hickey
- Published: Sep 17, 2026
- Type: Explainer

Advertisers often lose strategic intent when campaign briefs become data queries, while planners and data teams also miss insights hidden across large customer datasets. An AI-powered audience generation solution on the Databricks Data + AI Platform addresses this gap by combining Genie, custom tool-calling agents, Agent Bricks, Unity Catalog, and a Databricks App. Planners describe audiences in natural language; Genie produces governed SQL segments, an Affinity Agent calculates statistically validated lift against the full population, and a Supervisor Agent routes or chains these capabilities. The resulting workflow saves audiences in Unity Catalog for auditability and downstream activation, and is described as compressing planning and iteration cycles from days or weeks to minutes while enabling faster response to market shifts and pacing issues.


### [Rider and ReSharper 2026.2.2 Are Out!](https://yomu.fyi/post/rider-and-resharper-2026-2-2-are-out.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Alexander Kurakin
- Published: Sep 16, 2026
- Type: Announcement

Version 2026.2.2 of ReSharper, Rider, and .NET tools is available through the products, Toolbox App, or website. Rider adds an AI Agent Setup widget to the bottom-right status bar, where users can review supported MCP tools, skills, and hooks, see which are enabled, and open the relevant settings. Its hooks now give agents narrower inspection results for newly added code, while the Reformat file hook reports IDE formatting changes as a diff; dotCover also adds TUnit coverage, requiring JetBrains.dotCover.Framework and Microsoft.Testing.Platform 2.3.0 or later. The release fixes a macOS backend-resume freeze, removes dependence on Unity editor executable names for attachment, and enables MCP file operations across attached solution folders; ReSharper also receives TUnit coverage support.


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