---
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

### [Register Now for JetBrains GameDev Day 2026](https://yomu.fyi/post/register-now-for-jetbrains-gamedev-day-2026.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Anna Ruban
- Published: Sep 2, 2026
- Type: Announcement

JetBrains GameDev Day 2026 is scheduled for September 28 as a free online event for people building games, engines, tools, and production pipelines. The program covers Unity, Unreal Engine, Godot, proprietary engines, and engine-neutral infrastructure, with sessions on AI-assisted Figma-to-Unity integration, procedural content generation, ECS migration, cross-runtime C# and C++ debugging, Unreal performance profiling, and CI troubleshooting. The livestream starts at 10:00 am UTC, and recordings will be available on demand afterward; the full schedule and speaker details are still forthcoming. Watch parties are planned in Belgrade, alongside DevGAMM x FTW! attendee check-in, and at the JetBrains office in Limassol, with separate local timings and registration requirements.


### [Expanding Genie Agents: Deep analysis, file reasoning, and more](https://yomu.fyi/post/expanding-genie-agents-deep-analysis-file-reasoning-and-more.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Hanlin Sun, Sydney Sundell
- Published: Sep 2, 2026
- Type: Announcement

Genie Agents updates expand expert-curated business agents beyond quick factual lookups to deeper analysis across structured and unstructured data. Agent mode can create and refine research plans, issue iterative queries, and return reports with findings, visualizations, and citations; Agent mode APIs extend this workflow to custom applications, chatbots, scheduled reports, and internal tools through streaming Server-Sent Events. Agents can analyze PDFs, documents, slide decks, and images in Unity Catalog volumes alongside governed tables, with permissions and content search indexing applied to retrieval. Genie Code helps authors create baseline agents, diagnose conversation or benchmark failures, and identify knowledge gaps from user feedback, while authors retain review control over proposed changes. Shared chats keep follow-up messages and edited visualizations visible to authorized users, supporting collaboration and agent-quality review.


### [How we make AI coding more cost efficient without sacrificing task quality](https://yomu.fyi/post/how-we-make-ai-coding-more-cost-efficient-without-sacrificing-task-qua.md)
- Company: [Github](https://yomu.fyi/company/github.md)
- Author: Erik Kristensen
- Published: Sep 2, 2026
- Type: Explainer

GitHub Copilot’s AI coding efficiency work focuses on completed task cost rather than tokens in individual tool calls, because shorter responses can trigger recovery work. Using agentic coding benchmarks and controlled online experiments, the team evaluated selective output compression, removal of unused line-number prefixes, prompt shortening, and direct delivery of completed background results. The shipped compressor preserves source-like and arbitrary output, reorganizes search results without dropping content, and compresses repetitive install, build, test, and progress output while retaining a recovery path to originals. Removing line-number prefixes cut offline model-inference cost roughly 5% and online daily cost about 3%; direct background-result delivery reduced AI Credit usage about 2.3%, while prompt changes required workload-specific validation.


### [An Organizational Second Brain: Building an AI That Learns From Experts](https://yomu.fyi/post/an-organizational-second-brain-building-an-ai-that-learns-from-experts.md)
- Company: [Meta](https://yomu.fyi/company/meta.md)
- Author: By Shaurya Sengar, Jason Nawrocki, Jay Shah, Prashant Kommireddi
- Published: Sep 2, 2026
- Type: Problem & solution

Organizations often retain specialist knowledge in experts’ heads, leaving routine compliance assessments slow, inconsistent, and difficult to scale. The proposed “second brain” combines a structured, auditable knowledge architecture with a separate reasoning layer and a self-improvement loop that converts expert feedback into verified, regression-tested text updates without model retraining. High-density organizational positions, taxonomies, routing indexes, and gateway files are maintained in a dependency-linked filesystem, while sparse supporting material remains available through semantic or lexical retrieval. Composable recipes encode expert procedures, and an evaluation pipeline proposes auditable pull requests, retries failed fixes, and expands regression coverage after approval. After six weeks, SMEs reportedly found outputs useful almost always, assessments fell from days to minutes, and improvement cycles produced zero regressions; the architecture is presented as applicable beyond compliance.


### [Announcing the Databricks Big Book of AgentOps](https://yomu.fyi/post/announcing-the-databricks-big-book-of-agentops.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Pavithra Rao, Jeanne Choo, Kyra Wulffert, Alex Baur
- Published: Sep 2, 2026
- Type: Announcement

The Big Book of AgentOps presents AgentOps as an operating discipline for building, deploying and improving production AI agents, combining architecture, evaluation, observability, governance, security and cost management. It addresses the operational risks of tool calls, enterprise-data retrieval, API actions, retries and multi-step execution, where quality, permissions and spend can become difficult to control. The six-chapter book covers four agent architectures, four deployment patterns, a seven-phase lifecycle, DevOps practices, operationalization planning and stakeholder alignment, with Unity Catalog, Unity Gateway and MLflow central to its Databricks-based deployment guidance. Its practices include building evaluation datasets from real traces, calibrating automated judges with subject-matter-expert feedback, designing tracing and access controls into systems, and using RACI matrices to clarify ownership. The announcement cites customer examples including FactSet’s 44% accuracy improvement, DXC Technology’s 30% total-cost-of-ownership reduction, and ICE’s 77% syntactic accuracy and 96% execution matches.


### [4 engineering patterns behind the strongest AI Agents Challenge submissions](https://yomu.fyi/post/4-engineering-patterns-behind-the-strongest-ai-agents-challenge-submis.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Sergio Villani
- Published: Sep 2, 2026
- Type: Explainer

The strongest submissions in the Google for Startups AI Agents Challenge repeatedly used four engineering patterns rather than merely labeling prompt chains as multi-agent systems. They exposed internally used tools through bidirectional MCP, used typed events and asyncio queues to let independent agents run concurrently, and routed fallback outputs through one shared validation function. They also reduced inference cost with tiered routing: regex or a cheap classifier handled simple intent before requests reached an expensive reasoning model, with one team reporting more than 40 percent handled before a real model call. The post argues these practices improve bounded data access, latency, resilience, and cost without requiring larger teams or newer models, and notes that Agent Development Kit and Agents CLI submissions showed them frequently.


### [Google for Developers Blog - News about Web, Mobile, AI and Cloud](https://yomu.fyi/post/google-for-developers-blog-news-about-web-mobile-ai-and-cloud.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Published: Sep 2, 2026
- Type: Explainer

The page presents six featured Google developer articles spanning multi-agent systems, content credentials, TPU optimization, agent plugins, Go, and Genkit Go. One article identifies bidirectional MCP, async event buses, unified validation, and tiered routing as recurring patterns in successful AI Agents Challenge submissions. Another introduces Credentio, an open-source C++ library for local-first C2PA validation, while a HeyGen case study reports a 1.86x speedup for Avatar IV on Trillium TPUs using torchax, XLA, FSDP, Ulysses sequence parallelism, and custom kernel/compiler optimizations. The collection also covers Agent Plugins 1.0.0’s plugin.json directory specification and Genkit Go’s progressive-disclosure Agent Skills based on SKILL.md bundles. A final article argues that Go’s compiler, toolchain, readability, and backward compatibility provide guardrails for reviewing and maintaining AI-generated software.


### [How River takes security work from a fix to merge](https://yomu.fyi/post/how-river-takes-security-work-from-a-fix-to-merge.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: 2023-10-18
- Published: Sep 2, 2026
- Type: Problem & solution

Shopify’s River is an AI agent in Slack that manages dependency and first-party application vulnerability remediation beyond the patch. It validates ledger entries against repository, pull-request, and vulnerability-tracker state, checks whether findings remain active, and updates code on the repository head when it can do so safely. River regenerates lockfiles with repository tooling, repairs upgrade failures, discards stale CI results after rebases, and avoids overwriting human work. When product or risk judgment is required, it preserves evidence and hands owners a question, then verifies the repository head, dependency graph, tracker, and ledger after merge. In its 11 days, the dependency backlog fell about 70%, while security merges through the freshness-gated queue rose from about 10% to 80%.


### [How we eliminated $1 million a year of wasted AI agent spend in one hour](https://yomu.fyi/post/how-we-eliminated-1-million-a-year-of-wasted-ai-agent-spend-in-one-hou.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Alkis Polyzotis
- Published: Sep 1, 2026
- Type: Explainer

Databricks engineers investigated whether silent failures in MCP tool calls were driving hidden AI-agent costs as usage increased. Unity Gateway emitted OpenTelemetry traces for every invocation, recording tool names, arguments, errors, token counts, latency, and session IDs in a table that Genie One could query with plain-English questions. In a 24-hour sample, the analysis identified seven Jira and Google Drive/Docs bugs responsible for an estimated $499,000 annually in wasted tokens and 12,023 hours of agent wait time, or $1.2 million in lost productivity. The most costly patterns included under-specified interfaces, incompatible argument shapes, and cryptic or misleading errors that triggered retries and recovery loops. Engineers fixed the issues with a coding agent, completing the find-quantify-fix cycle in about an hour; the post recommends designing tools to tolerate reasonable LLM inputs and continuously tracing failures.


### [ClickHouse welcomes RunReveal](https://yomu.fyi/post/clickhouse-welcomes-runreveal.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Sep 1, 2026
- Type: Announcement

ClickHouse has acquired RunReveal, a company that built a security data platform on ClickHouse, and says the team’s expertise will become part of its work in security. The announcement frames security as a demanding data workload involving cloud audit logs, identity events, endpoint telemetry, and network flows, with continuous high-throughput ingest, years-long retention, and interactive query requirements. ClickHouse says the acquisition will bring sharper reference architectures, guidance on schema and retention design, and a clearer connection between customer requirements and its database roadmap, while RunReveal remains available through a bring-your-own-database model. Existing RunReveal customers will continue to receive platform support under unchanged contract terms, and the team’s work on agentic investigation is expected to inform support for analytics involving agents that hunt across sources, tune detections, and advance investigations.


### [Building an Adaptive Agentic Cybersecurity System with NVIDIA Nemotron](https://yomu.fyi/post/building-an-adaptive-agentic-cybersecurity-system-with-nvidia-nemotron.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Michelle Horton
- Published: Sep 1, 2026
- Type: Explainer

NVIDIA and CrowdStrike describe an agentic cybersecurity system that links red-agent attack execution with blue-agent detection engineering in a continuous offense-defense testing loop. In an isolated environment modeled on NVIDIA accelerated computing infrastructure, Falcon sensors captured telemetry while Nemotron 3 Ultra orchestrated the defensive workflow and a customized Nemotron 3 Super generated or repaired detections. The harness grounded agents in sensor schemas and attack traces, then applied linting, replay, structured correction, and independent review before deployment. Backtesting raised mean detection of the recorded attack from 16.5% to 41.9% with the optimized open pipeline across independently seeded sessions. In live-fire tests, five of 11 open detections detected an unseen attack, three qualified as gold, and those three covered all eight attacks; the authors call the result a directional case study because it used one scenario family and limited benign traffic.


### [Becoming an AI Team](https://yomu.fyi/post/becoming-an-ai-team.md)
- Company: [Pinterest](https://yomu.fyi/company/pinterest.md)
- Author: Pinterest Engineering
- Published: Sep 1, 2026
- Type: Explainer

Becoming an AI team requires more than adding assistants to existing workflows: it changes ownership, planning, roles, and execution. At Pinterest, infrastructure teams face the scale of serving billions of Pins, boards, ads, and real-time signals, making AI adoption an operational necessity for reliability, cost efficiency, and developer productivity. AI code-generation and transformation models can turn a widespread legacy-codebase refactor, such as replacing “foo” with “bar,” from a months-long effort into work completed and verified in a week or less. As routine execution is increasingly augmented or automated, engineers, product managers, and designers are expected to spend more time on strategy, problem definition, and user validation, while managers emphasize vision, trade-offs, mentorship, and collective impact. The conclusion is that teams must continuously optimize an AI-driven operating system rather than treat AI as a side project.


### [The Shared View That Makes Insurance AI Fast and Safe](https://yomu.fyi/post/the-shared-view-that-makes-insurance-ai-fast-and-safe.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Sep 1, 2026
- Type: Problem & solution

Agentic AI transforms insurance claims processing, underwriting, and customer service by autonomously executing tasks, reducing a routine claims review from eight hours to eight minutes. However, adding AI agents to fragmented data silos of policy details, claims history, and buried email threads multiplies problems because agents work with incomplete views that degrade. Pairing AI with a unified data architecture that creates a single shared context layer is key; MongoDB Atlas powers this by storing all data in a flexible document model. This shared view delivers accelerated resolution through single-motion retrievals, seamless human oversight by basing decisions on the same record, and built-in compliance through an automatic action trail. Insurers who build on this foundation can safely scale AI agents across claims, underwriting, and customer service while maintaining the oversight regulators and customers expect.


### [When AI takes the wheel: The rise of invisible identity intelligence in 2026](https://yomu.fyi/post/when-ai-takes-the-wheel-the-rise-of-invisible-identity-intelligence-in.md)
- Company: [Twilio](https://yomu.fyi/company/twilio.md)
- Author: Catie Kolander
- Published: Sep 1, 2026
- Type: Explainer

Point-in-time authentication treats an initial password, MFA, or passkey check as sufficient trust for the rest of a session, leaving post-login account takeovers, session hijacking, and risky API actions insufficiently evaluated. The proposed model replaces this front-door assumption with continuous context evaluation using passive telemetry such as network, telecommunications, device, behavioral, and transaction signals, then intervenes when risk scores or policy thresholds rise. For AI agents, delegated authority sets temporal, monetary, and API limits through short-lived credentials, while Know Your Agent lineage links execution to the authorizing human and deploying enterprise. High-impact actions can trigger human checkpoints through SMS, push, or voice channels, while immutable logs, transparent scoring, tenant isolation, and session lineage support auditability and compliance. The resulting architecture aims to keep low-risk autonomous work unobtrusive while making sensitive operations controllable and provable.


### [Running a Software Factory Efficiently at Uber Scale](https://yomu.fyi/post/running-a-software-factory-efficiently-at-uber-scale.md)
- Company: [Uber](https://yomu.fyi/company/uber.md)
- Published: Aug 31, 2026
- Type: Explainer

Uber describes a Software Factory in which AI tools and managed agents support software development, with more than 70% of pull requests attributed to local or cloud agents and over 3,600 agent skills. From February to August 2026, weekly active users grew sevenfold and weekly agentic requests 9.4-fold, while holding a model constant showed cost per 1,000 requests down almost 34% and cost per session down 52% from its June peak. The framework organizes usage into four layers and decomposes session spend into adoption, engagement, input and output workload, token pricing, and related execution factors measured through portfolio, model, driver, and managed-agent outcome metrics. Its optimization program uses real-work benchmarks, Pareto model selection, cheaper subagents, prompt caching, graph-grounded context, batched tool calls, and session-level waste analysis to reduce zero-value token use while improving or maintaining quality.


### [Securing Agentic Commerce](https://yomu.fyi/post/securing-agentic-commerce.md)
- Company: [Auth0](https://yomu.fyi/company/auth0.md)
- Author: Bradford Peirce
- Published: Aug 31, 2026
- Type: Explainer

Agentic commerce is moving from AI-assisted product discovery toward purchases completed by agents, but retail systems often cannot reliably establish the shopper’s identity or spending authority. The post describes four phases—product discovery, checkout functions, delegate-to-buy, and agent-to-agent commerce—and notes standards including UCP, ACP, and AP2. Auth0’s approach combines user authentication, asynchronous authorization, and authentication for MCP, using CIMD-based agent registration, OAuth, and on-behalf-of token exchange to issue temporary, scoped transaction access. It presents third-party shopping agents and retailer-owned agents as two deployment models, with actions tied to an authenticated shopper and recorded for auditability. The proposed identity layer can sit alongside an existing identity provider while supporting emerging protocols and future autonomous purchasing.


### [Run NVIDIA BioNeMo NIM Microservices for Protein Structure Prediction in Claude Science](https://yomu.fyi/post/run-nvidia-bionemo-nim-microservices-for-protein-structure-prediction.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Michelle Horton
- Published: Aug 31, 2026
- Type: Tutorial

Claude Science is used with the NVIDIA BioNeMo Agent Toolkit and NVIDIA NIM microservices to investigate how the predicted Seh1 structure changes alone versus with proposed partner C1HCX1. The workflow retrieves UniProt sequences, generates unpaired and species-paired multiple-sequence alignments with MSA Search, and sends those inputs to OpenFold3 and Boltz-2 for independent monomer and heteromer predictions. Using MSA input, heteromer iPTM reached 0.85 for OpenFold3 and 0.82 for Boltz-2, versus 0.14 and 0.19 without it; five-sample runs had standard deviations no greater than 0.006. Both models placed partner β-strands at Seh1’s WD40 propeller closure, while core Cα-RMSD remained 0.68 Å and 0.65 Å, respectively, but the predicted interaction remains experimentally unverified.


### [From Leaderboards to Model Profiles: A Deep Dive Evaluation of LLMs for Agentic Coding](https://yomu.fyi/post/from-leaderboards-to-model-profiles-a-deep-dive-evaluation-of-llms-for.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Dmitry Savelev
- Published: Aug 31, 2026
- Type: Benchmark

Resolve rate alone can hide how coding agents reach the same outcome. This evaluation compares Claude Opus 4.7, Gemini 3.5 Flash, GPT-5.5, and Qwen 3.6 27B FP8 through a Junie pipeline across four benchmark datasets, combining test results with trajectory, cost, efficiency, patch quality, and process measures. In one private benchmark, Opus and Gemini solved the same number of tasks, but Opus averaged 184 steps at USD 2.79 per run versus Gemini's 271 steps at USD 1.24. Across 523 tasks, Opus resolved 267 (51.1%) and Gemini 254 (48.6%); shared outcomes masked differences in navigation, validation, redundancy, hallucination, and patch scope. The wider comparison found GPT-5.5 had the highest resolve rate and always ran an executable check, Opus led patch-quality metrics, and Qwen delivered lower cost with lower resolution, while the authors caution that profiles depend on the Junie scaffold and LLM judges are diagnostic rather than ground truth.


### [Your customers don't hate AI — they hate you lying about it](https://yomu.fyi/post/your-customers-don-t-hate-ai-they-hate-you-lying-about-it.md)
- Company: [Twilio](https://yomu.fyi/company/twilio.md)
- Author: Jesse Sumrak
- Published: Aug 31, 2026
- Type: Explainer

Twilio research reports a major gap between brands’ and consumers’ accounts of AI-agent disclosure: 81% of brands say agents identify themselves immediately, while only 22% of consumers report that experience. Teams may treat a system-prompt line, widget label, or one-time channel message as disclosure, even though customers can miss it or encounter a different experience on SMS or voice. Recommended practice includes naming “AI agent” at the first turn, repeating disclosure across channels, explaining accessible data and decision logic, and offering an immediate route to a person. It also recommends checking transcripts rather than trusting configuration, using conversation analysis to verify whether disclosure fired. Research cited says 76% expect identification at the start, 49% want data-access transparency, and 54% trust AI more when its logic is explained.


### [Previewing the Model Hardware Standard](https://yomu.fyi/post/previewing-the-model-hardware-standard.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Aug 29, 2026
- Type: Announcement

Anthropic and HHMI Janelia Research Campus are opening a research preview of the Model Hardware Standard (MHS), a shared specification for AI agents to operate programmable laboratory and manufacturing devices safely. MHS replaces bespoke integrations with standardized drivers built around read and write primitives, device discovery, natural-language tags, generated reference files, and control through MCP, a command-line interface, or code files. The model-agnostic standard lets agents coordinate instruments, monitor operating data, adjust parameters in real time, and turn exploratory work into deterministic scripts; early projects covered protein assays, qPCR, microscopy, robotics, dose-response experiments, and quantum-laser stabilization. Reported results include serial dilution experiments running about three times faster and a QuEra laser-lock controller recovering 99.3% of the time, while the preview will inform safety evaluations and guidance before an open-source release.


[Newer posts](https://yomu.fyi/topic/ai-agents/page/11.md) · [Older posts](https://yomu.fyi/topic/ai-agents/page/13.md)