---
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

### [I got frustrated with AWS, so we built our own sandbox](https://yomu.fyi/post/i-got-frustrated-with-aws-so-we-built-our-own-sandbox.md)
- Company: [Boxcompute](https://yomu.fyi/company/boxcompute.md)
- Author: Farhan Helmy
- Published: Sep 16, 2026
- Type: Explainer

At a healthcare startup, an agent workflow built with LangGraph was constrained by context length and by the limited tools exposed to it for data analysis. Changing one node gave the agent access to Amazon Bedrock AgentCore Code Interpreter, allowing it to use pandas and other available data-science libraries in a sandbox. It could inspect files and outputs, write and run code, and adjust its analysis iteratively while keeping working data and intermediate artifacts in the workspace rather than sending every row through the conversation. Although the experiment demonstrated the usefulness of this controlled autonomy, regional availability forced the author to use a US region from Malaysia, motivating BoxCompute, an infrastructure project intended to provide agent workspaces with control over execution, resources, output collection, and cleanup.


### [Shared Selective Persistent Memory for Agentic LLM Systems](https://yomu.fyi/post/shared-selective-persistent-memory-for-agentic-llm-systems.md)
- Company: [Apple](https://yomu.fyi/company/apple.md)
- Published: Sep 16, 2026
- Type: Benchmark

Agentic LLM systems that generate code through multi-turn tool use lose configuration choices, domain constraints, schemas, and tool-use patterns when each session starts from zero. The proposed shared selective persistent memory retains task specifications, data schemas, tool configurations, and output constraints while discarding session-specific reasoning traces, and allows workspaces to be transferred across users with role-based access control. In a deployed collaborative workspace platform, agents create and maintain git-versioned dashboards, reports, and data-driven documents from CSV, SQL, REST API, and MCP sources; draft isolation supports risk-free changes and restoration without re-invoking the model. Results across three enterprise scenarios show 96% task completion, compared with 79% without memory and 71% with full history, while zero-token refresh cut recurring-update task time 14× and summary-driven generation reduced token cost 97× versus raw data injection.


### [Agent Anomaly Detection, now in Private Preview on the Gemini Enterprise Agent Platform](https://yomu.fyi/post/agent-anomaly-detection-now-in-private-preview-on-the-gemini-enterpris.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Achuth Narayan Rajagopal
- Published: Sep 16, 2026
- Type: Announcement

Agent Anomaly Detection is a reasoning-based oversight and audit layer for autonomous agents on the Gemini Enterprise Agent Platform, now in Private Preview. It analyzes existing reasoning traces, tool calls, execution flow, logs, and OpenTelemetry traces asynchronously, using a lightweight statistical pass followed by deeper LLM-based examination of flagged sessions. Its initial detectors cover tool misuse, identity and privilege abuse, cascading failures, rogue agents, resource exhaustion, and token usage escalation, with findings assigned severity, probability, explanations, and recommended fixes and surfaced in Security Command Center. In the Inventory Agent example, repeated large-batch, offset-jumping list\_inventory calls are classified as systematic scraping and produce a Critical resource-exhaustion finding at 95% probability; an API can let ADK callbacks or plugins block later actions. Custom business logic detectors are described as coming soon.


### [Beyond the Model: Engineering AI Infra with Scientific Judgement](https://yomu.fyi/post/beyond-the-model-engineering-ai-infra-with-scientific-judgement.md)
- Company: [Airbnb](https://yomu.fyi/company/airbnb.md)
- Author: AirbnbEng
- Published: Sep 15, 2026
- Type: Explainer

Airbnb describes Insight Miner, an agent harness designed to make unstructured data investigations scalable, reproducible, and audit-ready. Its problem was a bespoke process spanning data discovery, expert review, representative datasets, and manually curated methods that could not keep pace with new languages, geographies, and LLM-based products. The shared package combines extraction, embedding, and clustering with prompt tuning, hard-example mining, contrastive labeling, and a path from unsupervised exploration to classification, while an agent handles research questions, execution, and methodological guidance. Airbnb says investigations that once took months can now take days, allowing mechanical work to scale while data scientists spend more time inspecting ambiguous evidence, testing hypotheses, and improving techniques. The tool also lets nontechnical experts run analyses across use cases including survey coding, model evaluation, and fraud patterns.


### [From better privacy to our new ChatGPT plugin, here's what's new on Stack Overflow for Agents](https://yomu.fyi/post/from-better-privacy-to-our-new-chatgpt-plugin-here-s-what-s-new-on-sta.md)
- Company: [Stack Overflow](https://yomu.fyi/company/stack-overflow.md)
- Author: Phoebe Sajor, David Gibson
- Published: Sep 15, 2026
- Type: Announcement

Stack Overflow for Agents is an API-first knowledge exchange designed to address the “ephemeral intelligence gap,” in which agents lose solutions when a session’s context window disappears. The platform preserves agent-generated knowledge and adds trust scores, validation gateways, reputation-linked posts, and steering context to encourage verification and corrections. After three months, Stack Overflow has introduced a ChatGPT plugin that packages its existing skills into one installable OpenAI capability, plus Playbooks for structured procedural workflows distinct from Blueprints and TILs. New controls let users choose whether agents post locally, save drafts, request review before publishing, or post autonomously; read-only access supports proprietary work. The team says agents are already using and improving shared knowledge, but it is still seeking conclusive, quantifiable evidence of the platform’s value.


### [Your Agent Aced the Task. Will It Do It Again?](https://yomu.fyi/post/your-agent-aced-the-task-will-it-do-it-again.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Evelyn Duesterwald, Lilian Ngweta, Vatche Isahagian, Jayaram Radhakrishnan, Vinod Muthusamy, Gaodan Fang, Ashwath Vaithinathan Aravindan, Punleuk Oum, G Thomas, Merve Unuvar, Ayhan Sebin, Michał Ulewicz
- Published: Sep 15, 2026
- Type: Explainer

Agent reliability can be obscured by average benchmark scores: Mean@k reports average success, while Pass^k measures tasks succeeding on every repeated run. On AppWorld, a ReAct agent using GPT-4.1 achieved Mean@5 of 77.4% but Pass^5 of 53.0%, leaving a 24.4-point consistency gap, with temperature set to 0.0. The Consistency Analyzer diagnoses flip-prone decision points by resampling each step in one recorded trajectory with k=5 completions, requiring no ground truth, logits, or end-to-end replay. ALTK-Evolve turns flagged instability into reusable consistency guidelines that are retrieved at inference time. In the reported evaluation, these guidelines raised aggregate Pass^5 to 69.0% from 53.0% and Mean@5 to 81.0% from 77.4%, cutting the gap to 12.0 percentage points; similar-task Pass^5 also improved by 13.0pp.


### [Red Hat Developer Hub: Preventing compliance violations with AI coding agents](https://yomu.fyi/post/red-hat-developer-hub-preventing-compliance-violations-with-ai-coding.md)
- Company: [Red Hat](https://yomu.fyi/company/red-hat.md)
- Author: Evan Shortiss, Ben Wilcock
- Published: Sep 15, 2026
- Type: Explainer

AI coding agents can generate services quickly, but they may miss organization-specific architecture, ownership, and compliance rules stored in an internal developer portal. A demo connects Claude Code to a Backstage-based Red Hat Developer Hub through Backstage CLI actions, allowing the agent to query domains, components, APIs, software templates, and TechDocs before proposing a design. In a simulated Parasol Insurance catalog with more than 500 entities across 14 domains, the agent finds an existing but unrelated reconciliation service and resolves a Claims–Billing messaging conflict by keeping IBM MQ at the payment boundary and publishing internal events to Kafka. It then uses catalog relationships and specifications to identify integrations and produces a Technology Decision Summary that cites the sources behind each choice, making the resulting decisions reviewable and traceable.


### [What is CIAM in 2026 and why does it matter?](https://yomu.fyi/post/what-is-ciam-in-2026-and-why-does-it-matter.md)
- Company: [Twilio](https://yomu.fyi/company/twilio.md)
- Author: Ravleen Kaur
- Published: Sep 15, 2026
- Type: Explainer

CIAM has expanded beyond password storage and login-time credential checks as digital experiences include autonomous AI agents and attacks make point-in-time verification less reliable. Modern customer identity is presented as a continuous trust and control layer across web, mobile, messaging, voice, email, and AI interactions, balancing low friction with adaptive risk detection. The architecture separates an identity authority, which owns identity, federation, and authorization, from a trust orchestration layer that evaluates live signals, triggers step-up verification, adapts to mid-session risk, and records decisions. It also must define delegated authority for agents, human approval boundaries, multi-signal decisioning, explainability, consent, and identity resolution. Twilio is described as an adjacent execution layer providing Verify, Lookup, carrier intelligence, device fingerprinting, and passwordless or one-time passcode flows for high-risk moments.


### [Build zero-trust AI agents that judge intent, not just syntax](https://yomu.fyi/post/build-zero-trust-ai-agents-that-judge-intent-not-just-syntax.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Eric Dong, Shubham Saboo
- Published: Sep 15, 2026
- Type: Explainer

Part 2 of the Zero-trust Agents series addresses attacks that deterministic, build-time checks cannot recognize when syntax remains valid. It keeps the Customer Support and Returns Agent, which reads orders, calculates restocking fees in Agent Sandbox, and commits payouts through an issue\_refund tool signed with a hardware-backed Cloud KMS key, then moves enforcement to Gemini Enterprise Agent Platform. Agent Gateway applies three runtime controls: Model Armor screens prompts and responses, Semantic Governance Policies evaluates proposed tool calls against intent, history, and business rules, and Agent Anomaly Detection analyzes behavior across a session. The examples cover prompt injection, a non-refundable digital-license refund, and a multi-turn refund drain; detected anomalies can trigger a new policy that blocks subsequent calls without redeploying the agent.


### [Stop Rubber-stamping AI: A Contestable AI Framework for Regulated Industries](https://yomu.fyi/post/stop-rubber-stamping-ai-a-contestable-ai-framework-for-regulated-indus.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Sep 14, 2026
- Type: Explainer

AI deployed in regulated industries such as healthcare and manufacturing often lacks explainability and contestability, creating a barrier to adoption because responsible humans cannot meaningfully challenge AI outputs. The post presents contestable AI as an architectural pattern where multiple specialist agents debate recommendations, a qualified human reviews and overrides arguments, and the system re-scores in real time. In a clinical scenario, a patient analyzer agent retrieves similar cases, specialists argue for and against care options, and a general practitioner overrides as needed before a care plan is finalized. In manufacturing, a site reader agent ingests equipment sensor labels, two specialist agents debate classifications against a standard ontology, and an engineer overrides with context the model lacked, like a fixture offset. MongoDB’s document model stores the AI arguments, human overrides, confidence scores, and final decision in one retrievable document, supporting single-query decision lineage without joining data across systems.


### [From zero-shot forecast to purchase order with Amazon Bedrock AgentCore](https://yomu.fyi/post/from-zero-shot-forecast-to-purchase-order-with-amazon-bedrock-agentcor.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Hyunsoo Kim, Ph.D.
- Published: Sep 11, 2026
- Type: Problem & solution

This post presents an inventory automation architecture that moves from zero-shot demand forecasts to validated purchase orders while applying safety stock, minimum order quantities, budgets, and promotional adjustments. It combines Amazon Chronos2, hosted on SageMaker Serverless Inference, with four Claude-powered agents built using the Strands Agents SDK and deployed on Amazon Bedrock AgentCore; deterministic Python tools perform computation and agents handle judgment. Product data and future covariates arrive through CSV files in S3, while business rules remain in JSON, enabling what-if forecasts and onboarding without per-SKU training. In internal testing across 50 SKUs over a four-week horizon, the system reached 12.3% median WAPE, reduced onboarding from 2–3 weeks to under five minutes, and lowered estimated monthly inference cost from about $1,091 to $15. A conditional retry loop surfaces budget violations for user decisions instead of silently truncating orders, with traces and evaluator scores supporting auditability.


### [Debugging our AI search assistant with agent tracing](https://yomu.fyi/post/debugging-our-ai-search-assistant-with-agent-tracing.md)
- Company: [Sentry](https://yomu.fyi/company/sentry.md)
- Author: Dominik Buszowiecki, Shaun Kaasten
- Published: Sep 11, 2026
- Type: Explainer

Sentry’s Search Query Assistant converts natural-language prompts into Sentry Search Syntax, but its non-deterministic output requires evals and debugging when generated queries are wrong. A production issue caused searches on custom numerical attributes to return no events, so the team used Sentry’s AI Conversation view and Agents filters to find the relevant trace, inspect the timeline, and follow tool calls into the backend API. The trace showed that tool results were being incorporated into the system prompt with the field name bug\_\_predictions\_count instead of the required tags\[bug\_predictions\_count, number\] form. The team added an eval scenario, fixed the prompt generation, verified the corrected query locally and in production, and confirmed the updated prompt and API parameter in the trace. They also instrumented local eval runs to send data to Sentry, allowing debugging before a pull request.


### [Cognizant brings Claude to 350,000 employees](https://yomu.fyi/post/cognizant-brings-claude-to-350-000-employees.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Sep 10, 2026
- Type: Announcement

Cognizant announced plans to deploy Anthropic’s Claude to up to 350,000 employees globally, using it with agentic tooling, engineering platforms, and industry blueprints to move enterprise AI efforts from experimentation to production outcomes. It will make Claude available across corporate functions, engineering, and delivery teams, while Claude Code supports coding, testing, documentation, and DevOps workflows. The rollout also aligns Claude models, Claude Code, the Model Context Protocol (MCP), and the Agent SDK with Cognizant’s platforms for integration with existing data and applications, multi-step work, human oversight, and management of performance, risk, and spend. The stated applications include software productivity through Cognizant Flowsource, legacy modernization, multi-agent systems via Cognizant Neuro and Agent SDK, and regulated industry solutions beginning with financial services. Cognizant also describes responsible AI practices for deployment, monitoring, operations, and governance, and plans client workshops, reference patterns, and platform integrations to identify use cases and move them into production.


### [Claude in Microsoft Foundry and 365 Copilot](https://yomu.fyi/post/claude-in-microsoft-foundry-and-365-copilot.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Sep 10, 2026
- Type: Announcement

Anthropic and Microsoft are expanding their partnership by making Claude Sonnet 4.5, Haiku 4.5, and Opus 4.1 available in public preview through Microsoft Foundry, where Azure customers can build applications and enterprise agents. The integration uses serverless deployment, supports Foundry APIs, tools, and workflows, and works with existing Azure agreements, including MACC, while offering Python, TypeScript, and C# SDKs with Microsoft Entra authentication. Claude models are also available through Claude Code in Foundry, and Microsoft 365 Copilot integrations include Researcher, Copilot Studio custom agents, and a preview Agent Mode option in Excel. In Excel, Claude can generate formulas, analyze data, identify errors, and iterate on solutions. Foundry's Global Standard deployment uses standard API pricing; US DataZone availability is described as coming soon, and the models support capabilities including code execution, web search, citations, vision, tool use, and prompt caching.


### [Snowflake and Anthropic announce $200M partnership](https://yomu.fyi/post/snowflake-and-anthropic-announce-200m-partnership.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Sep 10, 2026
- Type: Announcement

Anthropic and Snowflake announced a multi-year, $200 million partnership that brings Claude models to Snowflake’s more than 12,600 customers across Amazon Bedrock, Google Cloud Vertex AI, and Microsoft Azure. The agreement also includes a joint go-to-market initiative for deploying AI agents in large enterprises. Claude can analyze structured and unstructured data through Snowflake Cortex AI, while Snowflake Intelligence and Cortex Agents support natural-language questions, multimodal SQL-based analysis, and custom multi-agent solutions under Snowflake Horizon Catalog governance. Snowflake’s internal benchmarks report greater than 90% accuracy on complex text-to-SQL tasks, and examples include Simon Data and Intercom. The partnership is positioned to help regulated industries move AI agents from pilots to production within governed data environments.


### [Donating MCP to the Agentic AI Foundation](https://yomu.fyi/post/donating-mcp-to-the-agentic-ai-foundation.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Sep 10, 2026
- Type: Announcement

Anthropic is donating the Model Context Protocol (MCP) to the Agentic AI Foundation (AAIF), a directed fund under the Linux Foundation co-founded by Anthropic, Block, and OpenAI, with support from Google, Microsoft, AWS, Cloudflare, and Bloomberg. Introduced as an open standard for connecting AI applications to external systems, MCP now has more than 10,000 active public servers, adoption across major AI products, enterprise deployment support, and official SDKs with 97M+ monthly downloads across Python and TypeScript. The donation places MCP alongside Block’s goose and OpenAI’s AGENTS.md as AAIF founding projects, while its existing governance model remains unchanged. MCP also has a community-driven Registry, and the November 25th specification release added asynchronous operations, statelessness, server identity, and official extensions.


### [Working with the US Department of Energy](https://yomu.fyi/post/working-with-the-us-department-of-energy.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Sep 10, 2026
- Type: Announcement

Anthropic and the US Department of Energy (DOE) announce a multi-year partnership under the Genesis Mission, targeting American energy dominance, biological and life sciences, and scientific productivity across potentially all 17 national laboratories. Anthropic plans to give DOE researchers access to Claude and engineers who can build purpose-built AI agents, Model Context Protocol servers connecting Claude with scientific instruments and tools, and Claude Skills for specialized workflows. Proposed applications include speeding permitting reviews, supporting nuclear technology research and energy security, developing pandemic and biological-threat early-warning systems, accelerating drug discovery, and using fifty years of DOE research to surface ideas and patterns. The partnership extends prior DOE work, including a nuclear risk classifier co-developed with the National Nuclear Security Administration and Claude’s rollout at Lawrence Livermore National Laboratory. Anthropic says future arrangements could develop a model for collaboration between AI and human researchers.


### [Why don’t machine learning research agents overfit?](https://yomu.fyi/post/why-don-8217-t-machine-learning-research-agents-overfit.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Martin Bertran Lopez, Aaron Roth
- Published: Sep 10, 2026
- Type: Explainer

Repeatedly optimizing against long-lived machine-learning benchmarks appears to invite overfitting, yet the post notes that gains on heavily reused benchmarks have largely transferred to newly created test sets. It describes a paper that resets LLM-based research agents, gives an explorer repeated access to validation feedback, and passes the resulting strategy through a token bottleneck to a fresh reproducer. Across eight datasets, strategies from hundreds of experiments were reproducible from prompts as short as 16 to 32 tokens; at shorter budgets, performance diverged, showing that the compressed message carried data-dependent choices rather than only prior knowledge. Deliberate overfitting occurred in 38 of 102 runs, with validation accuracy more than 10% above held-out accuracy, but those gains disappeared after compression; possible pretraining side channels remain unresolved.


### [Analyze MongoDB Atlas Data in OpenAI ChatGPT Work](https://yomu.fyi/post/analyze-mongodb-atlas-data-in-openai-chatgpt-work.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Sep 10, 2026
- Type: Announcement

MongoDB Atlas is now a connected data source in OpenAI’s Data Plugin, powered by the MongoDB Atlas Managed MCP Server. This integration lets analysts and business teams query real-time operational data in ChatGPT Work without leaving the conversation, such as monitoring conversion changes after a deployment, finding orders stuck in payment pending, comparing sales hours across regions, and reviewing customer activity before support calls. Insights can be turned into live charts and dashboards within the conversation. Access respects existing cluster controls via OAuth sign-in, with organization owners able to enable or restrict AI client access to read-only, and every query runs with the permissions of the connected user. Teams that trust MongoDB Atlas as their fully managed, real-time system of record can now question their data directly without waiting for ETL jobs.


### [Cursor Projects](https://yomu.fyi/post/cursor-projects.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Published: Sep 10, 2026
- Type: Announcement

Cursor is launching Projects, a beta feature for taking on larger bodies of work such as features, migrations, and full applications. Each Project preserves context over months, runs on its own cloud computer through Cloud Agents, and coordinates parallel subagents rather than having the coordinator write code itself. Shared files synchronize across agents’ cloud and local machines, storing research, artifacts, codebase knowledge, and preferred working practices so later agents can use them. Projects can also subscribe to Slack channels, schedules, or pull requests and delegate work when signals arrive, while local agents handle testing that requires the user’s machine. Projects are accessible from Cursor’s left-hand navigation and are rolling out to all users in beta starting today.


[Newer posts](https://yomu.fyi/topic/ai-agents/page/8.md) · [Older posts](https://yomu.fyi/topic/ai-agents/page/10.md)