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Only First party#MCP / tool use
Nov 14Sat
  1. Cloudflare Blog66

    Adapting to AI penetration testing orchestrators: The Modern Security Model

    Cloudflare reports that since the original ARTEX agentic pentesting framework was taken down on October 8, over 12,000 repositories and 50+ framework families have emerged, with daily requests for AI pentesting tools up 30x since October 1 and ARTEX and its forks making up over 60% of known orchestrators as of November 12. Cloudforce One observed incidents where stolen static CI/CD tokens were chained with these frameworks, including ARTEX creating artex-* Worker scripts and generating over 69 requests per minute and 400 database dump requests in an hour. The post recommends a six-pillar "Modern Security Model" covering WAF and traffic control, anomaly detection, Bot Defense, Identity-Gated Access and micro-segmentation, schema validation and mTLS, and threat intelligence.

    Why it matters: Offers a data-backed view of how quickly autonomous LLM pentesting frameworks are proliferating and a concrete defensive framework for security teams.

  2. Sierra Blog80

    Releasing Personal Agent Protocol 1.0

    Poppy (Personal Agent Protocol) v1.0 is now available, with Shopify enabling it for every merchant on its platform, Mercury, Synchrony and Turkish Airlines going live as design partners, and Muse, Meta's AI agent, checking for Poppy first on every site it visits. The protocol, built largely on existing standards like MCP, OpenAPI and UCP with OAuth authentication, defines how personal agents connect with companies, and now has more than 100 design partners after 60 new companies joined. Shopify co-authored Poppy's commerce extension linking it to UCP, and an implementation kit with a validator is available for any company to adopt the protocol.

    Why it matters: Announces a new agent-interoperability standard with concrete adoption details from Shopify, Meta and others, useful for tracking how personal agents connect to businesses.

Nov 13Fri
  1. GitHub Blog · AI & ML58

    Build once, run on any agent: A practical guide to agent skills

    GitHub's guide explains agent skills as versioned directories centered on a SKILL.md file that capture repeatable task instructions shareable across agents, while the environment still supplies tools, credentials, and permissions. Using examples like Proof Agent for adversarial verification and Data Forge for a multi-skill data pipeline, it recommends keeping project configuration in a manifest, sharing execution paths between skills, and placing human checkpoints and CI checks in workflows. It also covers packaging skills for clients such as Claude Code, Cursor, and GitHub Copilot, and notes that applications suit workflows needing stable interfaces, shared state, audit trails, or permissions enforced outside the model.

  2. AWS Machine Learning Blog47

    How Natera designed an agentic AI system for lab ops with Amazon Bedrock AgentCore

    Natera's lab engineering team built SWARM (Systematic Workflow Analytics and Resolutions Manager) on Amazon Bedrock AgentCore to capture and deploy institutional knowledge for lab instrument support, using two runtimes (swarm-agent and swarm-hive) that share a versioned library of 33 skills. The system reduced average first response time from 17.4 hours to roughly 8 minutes (about 130x faster) and delivered an automated first response on 96.2 percent of tickets, measured over a 90-day production evaluation period. The post details the architecture, including a custom document pipeline with Amazon Titan Text Embeddings and Amazon Bedrock Knowledge Bases, keyless OIDC-based deployment, Jira integration via EventBridge and Amazon SQS, and a continuous evaluation loop.

  3. Claude Blog78

    Introducing Claude Meetings

    Anthropic announced Claude Meetings in beta, letting Claude join video calls as a visible participant that answers questions aloud or in chat, builds docs and whiteboards with the team, and writes notes and action items. It is rolling out on Team and Enterprise plans with support for Google Meet, and each meeting gets a page in claude.ai with notes, transcript and artifacts. By default Claude stays out of calls with outside guests unless an admin allows it, and it saves a transcript, summary and screen descriptions rather than audio.

    Why it matters: Useful for teams evaluating AI meeting assistants, since it details capabilities, admin controls, privacy defaults and beta limitations.

  4. AWS Machine Learning Blog34

    Building a self-service LLM gateway with Amazon Bedrock and LiteLLM

    This post describes a pattern for a self-service LLM gateway that runs LiteLLM on AWS Fargate to expose Amazon Bedrock models through an OpenAI-compatible API, using Bedrock Application Inference Profiles for per-model quota isolation and scoped IAM access. It covers configuration of developer tools, LiteLLM model mapping with per-token costs for budget enforcement, CDK constructs for inference profiles, Langfuse observability, CloudWatch TPM alarms, and lessons learned from scaling to several hundred developers.

  5. Claude Blog58

    Introducing Claude for Manufacturing

    Anthropic launched Claude for Manufacturing, a plugin with 17 skills covering sales, quality, operations, supply chain, design, and setup, which connects to Autodesk Fusion, Infor, Microsoft 365, and Oracle NetSuite so Claude can draft quotes, investigate failed inspections, diagnose breakdowns, and compile production reports. Skills are designed to end at a person's review and approval, and Claude can link figures and claims back to underlying data and documents. The plugin does not operate equipment; Anthropic says it is developing the Model Hardware Standard as a research preview for programmable equipment.

  6. Replit Blog64

    Replit Agent can now buy for your business and help you reach customers

    Replit Agent now supports Agentic Commerce, powered by Stripe's Link and Visa, letting it find and prepare purchases for a business while the owner approves the final total before any charge. It also adds Agentic Ads with Google, which builds and launches campaigns across Search, YouTube, Display and Discover and can add conversion tracking. Agentic Commerce is available for Core and Pro users starting in the US for one-time purchases, while Agentic Ads is available for Pro users worldwide.

    Why it matters: Useful for readers tracking how coding agents are expanding into business operations like procurement and advertising.

  7. Databricks Blog58

    How Block powers collaborative AI agents with Buzz and Unity Gateway

    Block developed Buzz, an open source, model-agnostic collaboration space where employees and AI agents work together in group discussions, focused conversations, and one-on-one exchanges, with users choosing the model or coding harness behind each agent. Databricks Unity Gateway provides the model access layer, letting Buzz users select Databricks as their inference provider and choose from frontier models from OpenAI, Anthropic, and Google plus select open-weight models through a single integration. Block has also been experimenting with multi-agent workflows, such as a more capable model coordinating agents powered by smaller, more efficient models, and developed an internal benchmarking approach for these agent teams.

  8. AWS Machine Learning Blog48

    Fine-grained access control for Amazon Bedrock Managed Knowledge Base tools

    This post describes an architecture in which all employees share one agent and knowledge base, but each user can search only the documents they are allowed to view, using Amazon Bedrock Managed Knowledge Base ACL awareness exposed as MCP tools through Amazon Bedrock AgentCore Gateway. The design keeps the LLM from deciding who the user is: a request interceptor injects the verified email from the JWT into userContext, Policy in AgentCore checks it against the token's email claim, and a resource-based policy denies searches from principals other than the gateway execution role. It also covers prerequisites, ACL metadata files, tool schema configuration with parameterOverrides, and a verification script that tests each defense.

  9. AWS Machine Learning Blog52

    Build composable, interoperable multi-agent systems with A2A on AWS

    This AWS post presents a deployed reference architecture in which a LangChain-based enterprise orchestrator in one AWS account calls a Strands Agents-based compliance SaaS agent in another account using the Agent-to-Agent (A2A) protocol, with neither side depending on the other's framework or SDK. It covers cross-framework interoperability, bidirectional agent-to-agent callbacks, and OAuth authentication in both directions using separate Amazon Cognito user pools, with deployment from three Jupyter notebooks and an end-to-end test.

  10. Claude Blog40

    The Claude Tag guide for startups

    Anthropic's guide describes how eight high-growth startups use Claude Tag (beta) in Slack, covering common use cases such as on-call triage, turning threads into pull requests, a company knowledge base, self-serve data questions, and routine reporting. It includes example channels, connectors, starter instructions, and reported results such as a PR opened 21 minutes after a Slack mention and 134 merged PRs at NavigateAI. A getting-started checklist notes Claude Tag is in public beta on Team and Enterprise plans and requires an org Owner and Slack admin to connect.

Nov 12Thu
  1. Databricks Blog42

    Genie in Slack is now Generally Available: Governed AI cowork, right where work happens

    Databricks announced the General Availability of the Genie app for Slack, which answers questions in channels, threads, or DMs using the same governed context and permissions as Genie in Databricks, with every answer citing its sources. Users can @mention Genie in channels or DM it, and through MCP connectors Genie can create or update documents, comment on tickets, and update records with approval before each change, as well as schedule recurring analyses and work with files. Genie in Slack follows the same governance as other Genie surfaces, running each question with the asker's identity across Unity Catalog and connected sources, with admins able to govern AI usage centrally in Unity Gateway.

  2. n8n Blog22

    The future of work is built by everyone

    The article argues that centralizing automation in a small specialist team becomes a bottleneck as a company scales, and that the ability to automate one's own repetitive work should become a normal part of how people work, much as spreadsheets did. It describes n8n's move toward a guild-like Automation team with reusable templates, office hours and documentation standards, while acknowledging the approach is still early and that regulated industries may legitimately keep build access centralized.

  3. AWS Machine Learning Blog36

    Build autonomous AI agents with Amazon Bedrock and Spring AI

    This post shows how Java teams can build autonomous AI agents on the JVM using Spring AI and Amazon Bedrock AgentCore, with a single-agent travel assistant demonstrating tool calling, streaming, and memory, and a multi-agent intelligent document processing system with five specialist agents, a SpEL rules engine, and alerting. It covers the AgentCore runtime contract (@AgentCoreInvocation for POST /invocations and GET /ping), @Tool methods, structured output to Java records, memory advisors, and production patterns including Resilience4j retries, twelve-factor configuration, least-privilege IAM, Bedrock Guardrails, and Micrometer observability. Deployment uses an ARM64 image pushed to Amazon ECR and provisioned through modular Terraform, with an optional Amazon EKS and IRSA path.

  4. AWS Machine Learning Blog58

    Introducing the Web Search API on Amazon Bedrock AgentCore

    AWS announced the Web Search API on Amazon Bedrock AgentCore, which lets developers call the InvokeWebSearch operation directly from AWS SDKs or the AWS CLI with SigV4-signed IAM credentials, without creating a gateway or other resources. It returns up to 25 ranked results from an Amazon-operated web index, each with a text extract and, when available, title, URL, and publication time, and supports domain and publication-date filtering. It is available in six Regions and billed at $7 per 1,000 queries, complementing the existing AgentCore Gateway path.

  5. AWS Machine Learning Blog40

    How DXC Technology built a multi-model AI substrate on Amazon Bedrock

    DXC Technology built DXC OASIS, a harness for mission-critical IT operations, on a governed multi-model substrate in Amazon Bedrock that provides access to Anthropic Claude, Amazon Nova, Meta Llama, and open-weight models through one managed interface, with Bedrock Guardrails, AWS PrivateLink, and serverless scaling as system-level controls. DXC separates the substrate from its own orchestration, governance, and assurance layer, and reports that since general availability in March 2026, DXC OASIS has reduced resolution time by 95% on average across 56 measured use cases. The post describes the architecture, model routing, and authority tiers, and states that DXC is evaluating Amazon Bedrock AgentCore alongside its current orchestration layer.

  6. AWS Machine Learning Blog34

    Automating AWS Agent Registry updates with push-based architecture

    This post describes an event-driven pipeline using AWS CloudTrail, Amazon EventBridge, and AWS Lambda that detects Amazon Bedrock AgentCore UpdateAgentRuntime events and syncs MCP server tool schemas into AWS Agent Registry records, supporting both single- and multi-account setups. The Lambda extracts the runtime ARN, obtains an OAuth bearer token via AgentCore Identity, calls the MCP server's initialize and tools/list methods, then updates the matching APPROVED registry record only when normalized tool definitions differ. Updated records revert to Draft status and require curator approval before becoming discoverable, and the post notes limitations such as no automatic registration of new servers and no retry on cold starts.

  7. Google Developers Blog78

    A2UI 1.0: Build delightful, fast, reliable, agentic experiences

    Google released A2UI 1.0, the first stable version of its open protocol for generative UI, which lets AI agents respond with interactive screens that each app renders with its own native components. Version 1.0 adds bidirectional function calls, accessibility support and extensions, plus open-source renderers for React, Angular, Lit, Flutter, SwiftUI and Jetpack Compose (currently v0.9.1) and agent SDKs for Python, TypeScript and Dart. Atlassian, Alation, Bloomberg and ByteDance are already building with it, and the web and Flutter renderers work inside MCP Apps such as Claude and Gemini.

    Why it matters: It marks the first stable release of an open generative-UI protocol, with concrete renderer, SDK and partner details useful for developers evaluating agent interfaces.

Nov 11Wed
  1. Databricks Blog71

    Genie One: The AI cowork home for everyone

    Databricks announced that Genie One is now the enterprise home for AI cowork, combining Genie Search (hybrid AI search indexing tools like Google Drive, Jira, Confluence, Workday and SharePoint, plus over 100 MCP connectors), organizational directories, in-conversation artifacts such as slide decks and dashboards, MCP-based actions across tools, shareable skills, and native apps for browser, desktop, mobile, Slack and Microsoft Teams. Genie can now take more autonomous action on users' behalf, with Unity Gateway providing traces, MCP policies, permissions and cost management, and Genie One is free for users through January 31, 2027.

    Why it matters: It details a major enterprise AI assistant release, useful for readers tracking how agentic workplace tools are being packaged with governance and cross-tool search.

  2. Databricks Blog58

    Bring your business context to ChatGPT, Claude, Microsoft Copilot Cowork and Grok Bot

    Databricks introduced Genie plugins that give agents in ChatGPT, Claude, Microsoft Copilot Cowork and Grok Bot a governed interface to enterprise context, drawing on Genie Ontology for definitions, relationships and source context. Genie interprets business terms, finds permitted data, writes and runs SQL, routes specialized questions to domain Genie Agents, and returns answers with citations, with interactive visualizations in clients supporting the MCP Apps extension. Agents without native plugins can access Genie through the MCP server.

  3. Databricks Blog60

    From enterprise context to action: Genie Agents now analyze more data sources to transform your work

    Databricks announced that Genie Agents can now analyze unstructured files in Unity Catalog volumes, connect to enterprise tools via generally available native MCP connectors, and be shared outside an organization through OpenSharing and Genie Clean Rooms (both in beta). The post cites customers including Unilever, HelloFresh, S&P Global Energy, Atlassian, General Motors and ModMed, and describes how agents parse documents, cite specific pages, and combine file content with governed tables.

    Why it matters: Useful for readers tracking how enterprise AI agents are expanding from structured-data querying to document analysis, tool actions and cross-organization sharing.

  4. Databricks Blog74

    The new Genie: autonomous AI for complex enterprise work

    Databricks introduces the new Genie, an autonomous AI coworker that continuously handles long-running enterprise work, tracking progress, following up on missing inputs, and taking action as new information arrives, with users setting guardrails and approving where needed. It is supported by Genie Ontology for business context, Genie Search for hybrid enterprise search, Unity Gateway and Unity Catalog for governance, and new capabilities including artifacts, MCP connectors, Genie Agents, and plugins for tools such as ChatGPT and Claude. Databricks reports that in a new benchmark built with Turing, Genie answered correctly in 91% of task runs, 1.6 times the rate of the stronger frontier agent, while using less than half as many tokens.

    Why it matters: The announcement details a shift from task-level AI assistance to supervised autonomous agents, with concrete governance, context, and benchmark claims worth evaluating.

  5. OpenRouter Announcements66

    Introducing the Extractions API

    OpenRouter launched the Extractions API in alpha, which pulls named, typed fields out of text, images and PDFs, with launch models from Cloudflare (Clairic), Inception (Mercury Decide) and ScaleDown (Extract). Clairic supports all nine field types and returns spans, p_null and distributions for estimates and forecasts, while Extract handles PDFs with text and list fields and per-value confidence, and Mercury Decide handles text only at the lowest input price. The post walks through invoice, receipt and purchase-order examples with request and response payloads, and contrasts Extractions with the Decisions API, where the model distributes probability over answers you write.

    Why it matters: It gives a concrete, example-driven tour of a new extraction API, including per-model capabilities, pricing and the evidence fields (spans, p_null, confidence) that determine what to trust and what to route to a human.

  6. Mastra Blog58

    Introducing MCP Tasks in Mastra

    Mastra now supports MCP Tasks in its MCPServer and MCPClient, letting long-running tool calls run in the background so the agent loop stays unblocked while the client polls the server for status (working, input_required, completed, failed, or cancelled). Tools can be configured to run as tasks with "always", "auto" (after syncWindowMs, 2 seconds by default), or "off", and tasks can still use suspend() for human input. The feature requires @mastra/[email protected] and @mastra/[email protected] or later and is still experimental, so the API may change between releases.

  7. Hugging Face Blog55

    BotPsy: On-Device Personal Assistant with a Sub-Billion-Parameter Agentic Language Model

    Tether AI Research introduces BotPsy, a 0.8B-parameter model built on Qwen3.5-0.8B for function calling and agentic workflows, suitable for on-device deployment. It roughly doubles its base model's tool-use scores (BFCL v4 23.4 to 44.0, ACEBench 30.1 to 58.7, τ-bench 23.2 to 51.5) and outperforms every model up to 1.2B parameters tested on all six agentic benchmarks, while also beating Qwen3.5-2B-Think on five of the six. It will be released with open weights under Apache 2.0 in bf16, Q8 and Q4 as safetensors and GGUF files, and is intended for low- to medium-difficulty tasks, not high-stakes use.

  8. OpenRouter Announcements67

    MCP vs CLI vs Skills: What Each Costs in Context

    The article measures input-token costs for giving an agent GitHub access via the GitHub MCP server, the gh CLI, or a gh skill across Claude Haiku 5.5, Claude Sonnet 5.5, GPT-6 Luna, and Gemini 3.8 Flash; on Sonnet 5.5 the CLI used 1,421 tokens, the skill 3,315, and the MCP server 59,417 with its 46 default tools. It reports that the GitHub MCP server's 46 default tools add 19,436 tokens to every request on Sonnet 5.5, and that a single tool result (GitHub's release object) cost about 20,400 tokens. It also describes the beta Tool Search server tool, which cut the up-front cost of 46 tools to under 600 tokens and halved whole-task cost on Sonnet 5.5 while raising it on Haiku 5.5.

    Why it matters: It offers concrete, model-specific token and cost measurements for choosing between MCP, CLI, and skills, plus practical mitigations like scoping tools and trimming results.

  9. AWS Machine Learning Blog40

    Build a daily dependency watcher with LangGraph and Amazon Bedrock AgentCore web search

    The Web Search API in Amazon Bedrock AgentCore is now available, returning current web results with title, URL, publication date and query-relevant extract, signed with existing AWS credentials and served from an Amazon-operated index. The post walks through building a LangGraph dependency watch that researches npm, PyPI and container base images in parallel, keeping the date window, domain blocklist and search budget in code and checking each finding against its cited sources. In the test run, three branches made 12 searches and finished in 36 seconds versus 99 seconds sequentially, and the watch can be hosted on AgentCore runtime or offered through AgentCore Gateway.

  10. Pydantic AI Articles60

    Code mode is not a sandbox

    This post distinguishes code mode, where the model writes a short Python program calling your tools as functions via a single run_code tool, from a sandbox, which is an isolated environment where code runs. It describes three places a sandbox boundary can go (the whole agent, only the tools, or only the model-written code) and how Pydantic AI workspaces let you move that boundary without rewriting tools. It also covers Monty, a Rust-written Python interpreter that runs run_code next to the agent with no filesystem, network, or environment variables.

    Why it matters: Useful for teams deciding where to place isolation boundaries when combining code mode with sandboxed tool execution.

  11. AWS Machine Learning Blog34

    Autonomous treasury agents: How Trovata built an AI-native solution on Amazon Bedrock and AgentCore

    Trovata, an AI-native cash and liquidity management platform connected to over 10,000 banks, describes how it built autonomous treasury agents on Amazon Bedrock and Amazon Bedrock AgentCore, using role-based access control and a verified API layer within a bank-branded deployment model. The post outlines use cases such as cash forecasting, natural language querying, anomaly detection, automated reconciliation and persistent agentic workflows, and cites customer results including $1M+ in annualized cost savings, 5+ hours per day saved per accounting team, a 77 percent reduction in annual TMS costs, and reporting reduced from hours or days to under a minute. It also notes a library of ten agent patterns from real customer deployments and Trovata's plans to expand agent patterns, ERP integrations and global bank connectivity.

  12. Claude Blog72

    More ways to work with Claude: Sheets, Whiteboards, and Interactive Connectors

    Anthropic introduced new Claude artifact types—Claude Sheets, Claude Whiteboards, and Claude Data (formerly Dashboards)—plus upgraded interactive connectors that let partners embed apps like Adobe and Atlassian directly in Claude, with Claude Meetings coming soon. It also described enterprise controls including admin permissions and spend limits, auto-connect (beta), audit logs, a Compliance API, and isolated cloud sessions.

    Why it matters: Useful for readers tracking how Anthropic is expanding Claude from coding into collaborative, enterprise-wide work.

  13. Claude Blog74

    Your apps can now open in Claude, and Claude can work in them

    Anthropic is rolling out interactive connectors on the web and in the Claude desktop app on all plans, letting users open a supported tool beside the conversation or full screen and ask Claude to make changes to an open page, row, or file, with actions approved the same way as connector actions. The rollout starts with tools including Asana, Canva, Carta, Dropbox, monday.com, Smartsheet, Vercel, and Webflow, with Figma, Harvey, and Shopify building integrations. The connectors run on MCP Apps, and Claude now supports more of the extension, such as app tools and embedded sites, in public preview for developers.

    Why it matters: It documents a concrete expansion of Claude's connector capabilities and the MCP Apps developer surface, useful for readers tracking AI tool integrations.

  14. OpenRouter Announcements60

    How to Get Structured Output from Any LLM: JSON Schema, Tool Calls, or a Decision Model

    The article compares three routes to reliable structured LLM output: a strict JSON Schema in response_format, a forced tool call whose parameters are the schema, and a decision model for label-only fields. In tests on 24 support messages across nine schema-supporting models plus GLM 5.3 FlashX, strict schemas returned 209 of 216 valid replies (all 7 misses from one provider that ignores the schema), while prompt-only JSON failed 18 times due to code fences. It recommends require_parameters: true, Response Healing for broken JSON, and always validating replies yourself.

    Why it matters: A practical, test-backed guide to choosing between schema, tool-call, and decision-model approaches for structured LLM output.

Nov 10Tue
  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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 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. 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.