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Nov 11Wed
  1. Mastra Blog22

    Introducing Mastra FDE

    Mastra announced Mastra FDE, a forward-deployed engineering program open to any team building agents, in which a dedicated Mastra engineer embeds with the customer's team from architecture through production at 6 hours/week consultative or 20-40 hours/week hands-on. The post cites Salesforce's Agentforce Vibes harness, MongoDB's Sage platform, and Workday moving security operations agents from Bedrock Agents to Mastra in 31 days while cutting 3,000 lines of code and about 98% of estimated vector storage costs. It positions the offering as cheaper than one senior AI hire and backed by a shared Slack channel with Mastra's founders and engineers, roadmap insights, upskilling sessions, and Mastra Enterprise.

  2. NVIDIA Newsroom62

    NVIDIA Expands Open Source Commitment With OpenShell Donation and New AI Infrastructure

    At KubeCon + CloudNativeCon, NVIDIA is contributing its OpenShell agent security runtime to the CNCF as a Sandbox project and contributing to Google-led Agent Substrate, also accepted into the CNCF Sandbox. NVIDIA also announced new DSX AI Factory Platform components: NVIDIA Kubernetes Engine, NVIDIA Kubernetes Recipes and NVIDIA Snapshot, which restores checkpointed replicas in seconds without model reload or warmup. Design partners Crusoe and Saturn Cloud reported reduced workload readiness and vLLM startup times using Snapshot.

    Why it matters: It details concrete open source contributions and infrastructure tooling for running and securing AI workloads on Kubernetes, with named partners and measured results.

Nov 10Tue
  1. NVIDIA Technical Blog58

    Agent Substrate: Advancing Kubernetes for Agents at Scale

    AI agents spend most of their time idle, waiting on a human prompt, a tool result or a model response, and Kubernetes was not built for that. Running one Pod per session holds memory for inactive sessions, while deleting Pods between turns incurs a multi-second cold start and loses the agent's state, and RL post-training hits the same wall. Agent Substrate has now been donated to the foundation named in the source.

  2. Lovable Blog38

    Find it, fix it, ship it: building secure apps on Lovable

    Lovable outlines its security model: secure sign-in and access controls are on by default, secrets are encrypted, and apps are scanned on every publish for code, application logic, database schemas and row-level security policies, with a free deep scan available on demand. Users can select Fix to have an agent resolve a flagged issue, enable "Auto-fix security issues" in project settings, or use publishing controls to block releases with critical findings. The post also notes upcoming changes this month: a redesigned security experience and scheduled scans on all paid plans.

  3. Google Blog AI58

    Google Cloud launches Gemini for healthcare.

    Google Cloud announced Gemini for healthcare, an AI solution designed to unify fragmented clinical, operational, and administrative systems by turning siloed data into coordinated action. The platform integrates specialized skills and data connectors with existing third-party systems, drawing on electronic health records, billing platforms, clinical guidelines, and Medicare datasets to orchestrate multi-step workflows such as medical records synthesis and prior authorization review within defined guardrails and with human oversight. It is initially available in preview with launch partners including HCA Healthcare, Highmark Health, and Northwell Health.

  4. n8n Blog76

    Meet Nod: a standalone personal agent from n8n Labs that runs on your computer

    n8n Labs, a new team within n8n, has released Nod as a research preview: a standalone, open-source (Apache 2.0) personal agent for Mac and Windows that runs on your computer with its own local sandbox and browser, working in connected apps like Gmail, Calendar, Drive and Slack as well as websites with no integration. It remembers months of conversation (scoring 93.2% on LongMemEval), offers jobs it could take on, runs tasks in parallel via the app, Telegram or WhatsApp, and is free apart from model provider costs, with per-task receipts and a monthly spending limit.

    Why it matters: Useful for readers weighing a local, open-source personal agent, since it details Nod's sandboxed design, permission checks, memory benchmark and cost controls.

  5. Google DeepMind83

    Introducing AlphaProof Code: a breakthrough in verifying complex software at depth

    Google DeepMind researchers used AlphaProof Code, a multi-LLM agentic framework combining human expertise with AI agents, to formally verify key security properties of the actual code underlying the Signal protocol, generating over six million lines of Lean. The verification took four weeks, and when two previously fixed bugs were reintroduced into an isolated version of Signal's code, the system produced machine-checked proofs that the buggy versions were insecure. DeepMind is open-sourcing its formal verification artifacts.

    Why it matters: It reports a claimed first in formally verifying a widely deployed cryptographic protocol's implementation, with concrete details on method, timeline and bug-detection tests.

  6. AWS Machine Learning Blog42

    How skills and agents work in AWS Quick

    AWS Quick uses configurable AI agents that follow instructions, load reusable skills, act through connectors, and ground answers in spaces, knowledge bases, datasets, and dashboards. Skills follow the Agent Skills specification as folders with a SKILL.md file, and can be built from chat, published, and shared with teams. The post also covers best practices for production skills and the AWS Quick official catalog, including the Skill Architect skill.

  7. Claude Blog50

    JPMorganChase expands Claude Code to more employees across the firm

    JPMorganChase is expanding Claude beyond engineering to product teams, advisors and other employees, with infrastructure and operations teams using Claude Code for monitoring, alerting and root cause analysis and the security team using frontier models to find and fix vulnerabilities faster. Claude Code runs in controlled environments including the firm's Devspace platform, with staged access and an approval process for new models, and an automated gate enforces a hard pass or halt so Claude cannot sign off on its own work. In Asset & Wealth Management, small teams are using Claude Code in their engineering workflow, and J.P. Morgan Asset Management recently announced a connector bringing 55ip's ActiveTax Technology into Claude for Financial Advisors.

  8. Browser Use Blog46

    Autofill Passwords with 1Password and Browser Use

    This tutorial explains how to connect 1Password to Browser Use Cloud via a read-only service account so an agent can fill passwords and TOTP codes, using a dedicated vault and a public test site (The Internet) to verify a login. It covers creating the vault and service account, connecting the token in Browser Use, running the login task through the dashboard or the V4 REST API, and troubleshooting common failures.

  10. Pydantic AI Articles50

    The best Python agent harnesses in 2026

    This roundup compares Python agent harness SDKs — libraries that supply file and shell tools, planning, sub-agents, context management and sandboxes around a model — judging each on model portability, how much can be taken apart, where commands run, approvals, durability, long-run context handling, tracing and commercial support. Pydantic AI Harness is named the overall pick, with LangChain Deep Agents, the Claude Agent SDK, OpenAI Agents SDK, Codex SDK, OpenHands Software Agent SDK, Strands harness and Microsoft Agent Framework Harness each recommended for specific stacks. The piece states its figures were read from each project's own docs and repository on October 30, 2026, with the Pydantic AI Harness and OpenHands Index figures re-read on November 9, 2026.

  11. AWS Machine Learning Blog72

    Introducing Claude Fable 5.2 on AWS

    Anthropic's Claude Fable 5.2 and Claude Mythos 5.2 are now available on Amazon Bedrock and Claude Platform on AWS, with Fable 5.2 positioned as Anthropic's most capable generally available model for coding, math, and science, and Mythos 5.2 offered through verification programs for cyber security and life sciences work. Fable 5.2 is designated a Covered Model requiring up to 30 days of data retention and human safety review by default, with an aws_review retention mode set on the account or project before invocation, while Enterprise Frontier Safeguards combining zero data retention with misuse detection is in development. The post includes a Boto3 example using model ID global.anthropic.claude-fable-5-2 and describes the claude-api skill in Claude Code for migrating existing codebases.

    Why it matters: Useful for teams planning to adopt or migrate to Fable 5.2 on Bedrock, since it details retention requirements, fallback behavior, and migration tooling.

  12. AWS Machine Learning Blog38

    Single-shot and agentic retrieval with Amazon Bedrock Managed Knowledge Base

    Amazon Bedrock Managed Knowledge Base now supports single-shot RAG and retrieval-only requests through AgenticRetrieveStream, alongside existing agentic retrieval, letting one API serve simple and complex questions. The maxAgentIteration range is now 0–5: 0 returns passages with no model calls, 1 performs one retrieval plus one generated answer, and 2–5 run up to n minus 1 planning rounds before answering. The post illustrates each mode with a fictional banking policy assistant and advises choosing the lowest iteration limit that meets each workflow's quality needs.

  13. Claude Code Releases42

    v2.1.321

    Claude Code v2.1.321 adds Claude Fable 5.2 (claude-fable-5-2) as the default Fable model with 1M context and $8/$40 per Mtok pricing, adds a --settings <file> option to claude remote-control, and fixes numerous bugs including MCP OAuth secrets being sent to another host on redirect, sandbox and permission-check issues, and network-path editing problems. It also changes mods to turn on without asking in auto and bypass modes, moves saved Fable 5.1 picks to fable, and adjusts fast mode labels, nested skill loading, and gateway and Windows behaviors.

  15. AWS Machine Learning Blog32

    Professional work deserves professional tools. Meet AWS Quick.

    AWS announced its first brand campaign for AWS Quick, an AI workspace built only for work where teams use AI agents to research, analyze, and act across the data and applications they already rely on. The campaign runs in the United States and the United Kingdom on streaming TV, online video, social media, and leading business publications, and cites enterprises including 3M, BMW, Mondelez, New York Life, and Southwest Airlines as users. AWS says Quick has no consumer version and integrates with security and compliance tools such as Microsoft Purview, AWS IAM, and Microsoft Active Directory, and carries certifications including HIPAA, FedRAMP, SOC 2, and ISO 27001.

  16. AWS Machine Learning Blog38

    How CrossnoKaye grounds physical AI with Amazon Bedrock AgentCore

    CrossnoKaye's ATLAS software-defined automation platform and its AURA agentic system run on AWS, using Amazon Bedrock AgentCore code interpreter microVMs to execute model-authored Python for verifiable statistics and physical balances across plant data. AURA operates in three modes—interactive Chats with warm sandbox sessions, repeatable Tasks, and automated Routines—and enforces layered safety through Bedrock Guardrails, read-only air-gapped sandboxes, deterministic edge base control, and independent hardware protection. The post describes the architecture and reports results such as investigations completing in under four minutes and a 34 percent reduction in batch drying-time standard deviation.

  17. AWS Machine Learning Blog47

    Pluggable Agentic AI Framework: A seven-layer composable enterprise agentic AI reference architecture on Amazon Bedrock AgentCore

    AWS presents a seven-layer composable reference architecture for multi-agent AI applications—Data and Knowledge, Model and Inference, Orchestration, Security and Guardrails, Application, Observability, and Agentic Operations—each with a distinct responsibility and stable contract, illustrated by a retail customer support reference implementation using a supervisor agent delegating to Order and Refund specialists. The post argues that pluggability at layer boundaries lets organizations swap models, tools, and providers without redesigning the stack, and that security, governance, observability, and continuous evaluation should be designed in from day one rather than added after the system is built.

  18. Google Developers Blog58

    From prompt to app: Build with Google Maps in AI Studio

    Google Maps Platform is now available in Google AI Studio, letting users describe an app idea and have AI Studio handle configuration and API key management with no credit card required. The post lists core APIs for Maps, Places, Routes and Environment, plus example builds like a City Immersive Tour, Pet Grooming Route Optimizer and Store Locator. Built-in sandbox quotas of 100 calls per day per API reset every 24 hours, and linking a Google Cloud billing account unlocks production-grade quotas.

  19. Databricks Blog23

    Turn buying signals into pipeline with Sendoso gifts in Genie One

    Databricks describes how Sendoso's MCP server, installed from Databricks Marketplace as a governed MCP Service in Unity Catalog, lets Genie One users find intent signals in their own data and then select, preview, approve, track or cancel a gift without leaving the workspace. Send history sits alongside pipeline data for attribution, while Unity Catalog permissions, Unity Gateway controls and Sendoso budgets and approvals govern each send. The post cites Sendoso's 2026 Digital Fatigue Report finding that 84% of buyers are fatigued by cold calls and 83% by marketing emails.

Nov 9Mon
  1. ElevenLabs Blog60

    ElevenAgents Campaign Manager works every contact to an outcome

    ElevenLabs introduces ElevenAgents Campaign Manager, which takes a list of contacts and a target outcome and runs outbound campaigns, following up over time and carrying context between calls until the outcome is reached or the contact asks to stop. It offers campaign analytics with per-contact decision timelines, a needs-attention queue, CSV export, and controls including calling windows, attempt limits, do-not-call lists and disclosures, and it uses existing ElevenAgents agents and phone numbers over phone calls, WhatsApp voice and WhatsApp messaging, with email and SMS coming soon.

    Why it matters: Announces a new outbound campaign product from ElevenLabs with beta customer results, so it is relevant to teams evaluating AI calling tools.

  2. Lovable Blog33

    The Radish AI team joins Lovable to scale agentic capabilities for businesses

    Lovable has acquired the team behind Radish AI, which built an agentic infrastructure platform for building, deploying, and scaling complex AI agents with privacy and security built in. The team includes Nirman Gupta (formerly of Meta and Bloomberg), Ish Singh (a two-time founder who led AI and data products at Google, JPMorgan, and Goldman Sachs), and Adrian Zgorzałek (12 years at Meta). They will be split between Lovable's Stockholm and London offices, with Gupta leading engineering on agents for businesses from the London office.

  3. NVIDIA Newsroom34

    Korea’s AI Ecosystem Advances and Secures Agentic AI With NVIDIA

    Hyundai Motor Group is exploring NVIDIA NemoClaw blueprints and the NVIDIA OpenShell runtime, while AIM Intelligence is integrating its Starfort agent control platform with NVIDIA OpenShell to combine runtime access controls with detection of malicious instructions and data leakage. LG AI Research is opening API access for K-EXAONE-2.5, developed using NVIDIA Nemotron datasets including Nemotron-Personas-Korea, an open dataset of 7 million fully synthetic Korean personas grounded in official Korean statistics. OpenShell, part of the NVIDIA Open Agent Safety Platform, provides a secure runtime with sandboxed execution and policy-based controls for governing autonomous agent actions.

  4. Artificial Analysis Articles42

    Introducing the Artificial Analysis Decision Index v2

    Artificial Analysis released Decision Index v2, with 45% of the Index built on private datasets (up from 25% in v1) and a new Decision Score that rewards both correctness and calibrated confidence. Drex Max v1.1 (Nace AI) and GLiDE v1.2 (Fastino Labs) both score 73, with Mammoth 27B (Baseten) at 72, while cost per 1M Tasks ranges from $14 for Celeris-1.1 Decision to $859 for Clef. The update adds AA-Paperclip (1,500 private documents), AA-Symmetry, revised AA-Omniscience-Decision and HLE-MultiChoice benchmarks, and EvalSafe-O*NET-AA, while reducing When2Call to 10% and removing WildGuardTest.

Nov 8Sun
  1. n8n Blog20

    Send contracts for e-signature from n8n, with TurboDocx, Slack approval and SharePoint filing

    This guest post by TurboDocx CEO Nicolas Fry walks through an n8n workflow that reads contracts marked "Ready to send" from an Excel table, generates the agreement from a Word template via the TurboDocx node, and routes it to Slack for Approve & send / Reject approval. Approved contracts are sent for e-signature with signature and date fields placed at template anchors, and a TurboDocx Trigger then downloads the executed PDF, uploads it to SharePoint, marks the row Signed and posts to Slack. The post covers setup, template preparation, end-to-end testing, troubleshooting and possible swaps such as CRM triggers or Teams approvals.

Nov 7Sat
  1. GitHub Blog · AI & ML36

    Beyond code generation: A developer’s guide to agentic engineering

    GitHub reports over 1.2 billion commits in one week this October, more than 400% above a year earlier, while a Faros AI study found pull requests spent 441% longer in review as teams moved from low to high AI adoption. The post outlines an agentic engineering workflow across Plan, Build, Verify, and Observe, describing Copilot features such as Plan Mode, cloud sandboxes, session continuity, stacked pull requests, Copilot code review, Agentic Autofix, and Agent Merge. It advises starting with one frustrating piece of work and putting an agent on it before expanding across the process.

  2. ElevenLabs Blog30

    Voice AI infrastructure: The 2026 stack explained

    This guide describes the layers of a production voice AI stack—speech recognition, turn-taking, language models, orchestration, tools and MCP, knowledge and memory, speech synthesis, telephony, and guardrails, testing, and monitoring—and explains how ElevenAgents implements each, using Eleven v4 as the example voice layer. It covers two build modes (Custom and Realtime), Scribe v2 Realtime transcription at roughly 150 ms, a proprietary turn-taking model, workflow orchestration nodes, MCP tool approval modes, RAG latency reduced from 326 ms to 155 ms, and Eleven v4 Turbo figures of about 100 ms inference latency and about 150 ms time to first speech. It also details Guardrails 2.0, simulation and tool-call tests, A/B experiments, and observability metrics.

  3. Cursor Changelog62

    Voice Chat with Agents

    Cursor now supports realtime voice calls with agents in the Agents Window, the IDE, on web, and in Cursor for iOS, letting you describe changes out loud, ask what an agent is working on, and hear updates as work progresses. Voice chat runs on Grok Voice with selectable voices including Ara, Eve, Leo, Rex, and Sal, and uses zero data retention when Privacy Mode is on. It is separate from dictation, which still converts speech to text in the chat input and toggles with Cmd+Shift+Space in the IDE.

    Why it matters: Useful for Cursor users who want to know how the new voice call feature differs from dictation and where it is available.

  5. AWS Machine Learning Blog40

    Serverless MCP server for AI agent data lake access on Amazon ECS Fargate

    Amazon's North America Sort Center team built a serverless Model Context Protocol (MCP) server on Amazon ECS Fargate that acts as a centralized, agent-agnostic gateway between AI agents and the enterprise data lake. Adding a dataset requires only a metadata JSON file, which the pipeline indexes into Amazon Bedrock Knowledge Bases, while Amazon Cognito authentication, SELECT-only Athena tools, Bedrock Guardrails, and CloudWatch logging provide centralized controls. The architecture separates the tool layer from the agent layer so any MCP-compatible client can share the same tools and access controls in a multi-tenant environment.

  6. AWS Machine Learning Blog36

    Taxonomy of agentic primitives in AWS Quick

    AWS Quick's team describes a taxonomy of agentic primitives built from four questions—When, What, Who, and How—covering triggers (human-initiated, agent-delegated, scheduled, event-driven), agents as persistent learning actors with instructions, capabilities, and learned context, and capabilities spanning tools, skills, and workflows. The post argues skills and workflows retain value independent of model improvements, with skills able to crystallize into workflows and workflows dissolve back into skills, and illustrates how a single request like a daily Slack digest can be met at different points on a confidence contract.

  7. Mastra Blog38

    Introducing Ring Signals for Mastra Agents

    Mastra agents can now receive Ring doorbell events as notification signals with configurable priorities, letting the agent fetch a snapshot or video clip from the Ring API and decide what to do. Setup requires a Ring developer app, the @mastra/ring-signals package (0.1.0) and @mastra/core 1.81.0 or later, and RingSignals can target an existing channel thread such as iMessage or be followed via the client SDK's subscribeToThread().

  8. Claude Code Releases20

    v2.1.319

    Claude Code v2.1.319 changes auto mode so calls the server can't check are asked about or denied when telemetry or feature flags are off, adds MCP task backgrounding in the terminal with a CLAUDE_CODE_DISABLE_MCP_TASK_BACKGROUND opt-out, and introduces a permissions.denyInsteadOfPrompting setting. It also adds /claude-api latency-optimize, spend breakdowns in /usage, several Claude apps gateway admin settings, and a long list of fixes spanning MCP, /diff, permissions, sandboxing, self-hosted runners, VSCode and cloud sessions.

  9. Databricks Blog38

    Real-time fraud detection with Feature Views

    Databricks describes how Feature Views in its Feature Store let teams define a feature once and use the same definition for point-in-time-correct training data and managed online-serving pipelines, reducing training-serving skew and data leakage. The walkthrough follows a $900 payment from a Kafka stream through rolling and Sawtooth window aggregations, an on-demand deviation feature, model training and Model Serving, with Unity Catalog providing governance and lineage.

  10. Databricks Blog30

    How public sector organizations can govern AI at scale

    Databricks outlines why public sector AI projects stall on governance questions—model access, data residency, and spend visibility—and describes how its Unity Gateway provides centralized controls over which models, coding agents, and tools teams can use, with per-user or per-team spending caps, platform-level compliance policies, and geographic data residency for hosted foundation models. The piece argues that agencies which solve governance centrally before scaling adoption can match each mission to an appropriate model while keeping data, users, and budgets under control.

  11. AWS Machine Learning Blog42

    Extend Claude Code to remote sessions on Amazon Bedrock AgentCore with security controls

    This post explains how to run Claude Code as a remote agent on Amazon Bedrock AgentCore runtime, where each task executes in its own isolated microVM session inside your AWS account and returns a GitHub pull request for human review. It walks through the accompanying sample-remote-coding-agent, covering the architecture (API Gateway, Lambda, DynamoDB, SQS, AgentCore runtime) and security controls including Bedrock Guardrails prompt screening, short-lived GitHub tokens via AgentCore Identity, private VPC networking, and least-privilege IAM. The sample suits scoped features, bug fixes, and tests on trusted repositories, but not untrusted code or open-ended work, and the agent never merges pull requests.

  12. AWS Machine Learning Blog36

    Taxbit Assist: A grounded, role-aware AI agent on Amazon Bedrock AgentCore

    Taxbit built Taxbit Assist, an embedded AI assistant for its digital-asset tax compliance dashboard, on Amazon Bedrock AgentCore using the Strands Agents SDK, with grounded and cited answers, role and organization awareness, no tax advice, and regional data residency. The post details the architecture, a curated knowledge base with semantic chunking and reranking, identity bound in code rather than by the model, guardrails-based PII handling, and continuous production quality measurement. Taxbit reports the assistant has run in the US and EU since May 2026, with model cost averaging under 20 cents per conversation and about three-quarters of input tokens served from prompt cache.

  13. AWS Machine Learning Blog50

    How Greenlight Financial built an autonomous SOC solution on Amazon Bedrock AgentCore

    Greenlight Financial's Luna, built on Amazon Bedrock AgentCore runtime and memory, autonomously resolves more than 90 percent of its 3,000–4,000 monthly security and risk cases, using a tiered supervisor architecture with over 30 specialized agents and episodic memory for continuous learning. The system triages dark web breach-card alerts, identity anomalies, and financial-risk events in seconds, escalating complex cases to a Tier 2 supervisor and human analysts. Greenlight later extended Luna into financial risk triage within two months by adding new specialized agents and a risk-domain supervisor to the existing infrastructure.

  14. AWS Machine Learning Blog50

    Govern AI agent access with Okta Cross App Access and Amazon Bedrock AgentCore – Part 1

    This AWS post describes using Okta Cross App Access (XAA) as the governance plane and Amazon Bedrock AgentCore Identity as the execution plane so an AI agent can act on behalf of signed-in users, with Okta deciding which agent may act for which user and AgentCore redeeming Okta's single-use ID-JAG for a short-lived access token. It walks through registering the agent in Okta, configuring the credential provider and workload identity, enforcing per-user authority via the groups claim in MCP servers, and reports measured results such as revocation taking effect in about 1 second and AgentCore Identity adding about 0.6 seconds to a roughly 30-second request.