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All news · Today1 items

#MCP and tool calling

All collected and published items, ordered by time. Picks are a selected subset.
First-party

Direct announcements from model vendors, companies and research institutions on their official sites, blogs and accounts.

Today10/17Sat
  1. AWS Machine Learning Blog60

    Building trust in AI agents: A production-ready evaluation framework with Amazon Bedrock AgentCore

    AWS explains how its SMGS Operations team built a nine-stage evaluation framework with Amazon Bedrock AgentCore for an internal sales intelligence system. The framework combines domain-specific evaluators, versioned golden datasets, CI/CD gates, continuous monitoring, and trace-driven improvements across tools, agents, and multi-agent workflows. AWS reports that automated evaluation reduced quality regression detection from days or weeks to under four hours.

    Why it matters: The framework offers a transferable way to connect domain-specific failure categories with release gates and production monitoring across tools, individual agents, and multi-agent workflows.

10/15Thu
  1. AWS Machine Learning Blog58

    Build self-improving document processing using Amazon Bedrock AgentCore

    AWS explains how to build a document processing system using Amazon Bedrock AgentCore and eight specialized agents that classify documents, extract data, validate results, and save refined extraction instructions for reuse. In its purchase order example, processing takes 3 minutes 49 seconds for an unfamiliar format and 1 minute 30 seconds for a subsequent document using stored instructions. The guide includes deployment steps and explains how to customize schemas, validation logic, and prompts.

10/14Wed
  1. AWS Machine Learning Blog62

    Building Agents That Transact With Anchor Browser and AgentCore Payments

    The guide explains how Anchor Browser integrates with Amazon Bedrock AgentCore payments to handle x402-protected pages and APIs within the same browser session. Setting session.x402 to true enables automatic payments subject to configured spending and expiration limits, while wallet credentials remain outside the agent runtime. Existing browser automation code can continue unchanged; rejected payments or unsupported challenges leave the original HTTP 402 response intact.

    Why it matters: The integration illustrates a transferable architecture for paid agent workflows by placing payment signing and budget enforcement outside the agent runtime while preserving browser session context.

10/13Tue
  1. AWS Machine Learning Blog56

    How ShipStation Global built an AI IT help desk on Amazon Bedrock AgentCore

    ShipStation Global built Deckhand, a Slack IT help desk agent on Amazon Bedrock AgentCore, to answer questions, analyze error screenshots, and create or update Jira Service Management tickets. The implementation guide explains its Strands agent, knowledge retrieval, MCP tools, and extraction of resolved Slack threads using DSPy’s Recursive Language Model. In its first three months, 812 of 1,221 agent conversations were handled in chat, while 409 became tickets; positive feedback rose from 82 percent in April to 96 percent in June.

  2. GitHub Blog · AI & ML49

    How to Teach GitHub Copilot About Your Codebase

    GitHub’s beginner guide explains four ways to tailor Copilot to a project: custom instructions for repository conventions, agent skills for repeatable procedures, custom agents for broader specialized tasks, and MCP servers for connecting to external tools and data. It recommends starting with a convention you often explain and adding it to `.github/copilot-instructions.md`.

10/9Fri
10/7Wed
  1. AWS Machine Learning Blog36

    How Cornerstone OnDemand cut database diagnosis time by 78% with Amazon Bedrock

    Cornerstone OnDemand built Orion AI, a multi-agent system based on Amazon Bedrock and the open-source agent orchestration framework Strands Agents. It reduced database diagnosis time from 45 minutes to 10 minutes, a 78% reduction. It also compressed manual lifecycle operations from more than 10 steps to one interaction, a 70% reduction, and cut median redundant alerts by 65%. A three-person team delivered it in six months.

  3. AWS Machine Learning Blog60

    How to build a context-aware AI assistant on AgentCore and OpenClaw

    An AWS blog post explains how to run the open-source agent system OpenClaw on the Amazon Bedrock AgentCore runtime to build Sprout, a personal assistant with long-term memory.

    Why it matters: The source provides reusable methods for container wrapping, namespaces, and prompt-cache ordering, helping readers build an agent with long-term memory.

10/6Tue
  1. AWS Machine Learning Blog34

    Amazon SageMaker AI launches aws-ai-ml skill to optimize generative AI inference from coding agents including Kiro, Claude Code, and Codex

    Amazon SageMaker AI launched the aws-ai-ml skill through Agent Toolkit for AWS, giving MCP-enabled coding agents such as Kiro, Claude Code, and Codex inference-optimization and benchmarking capabilities. They can benchmark endpoints, recommend deployment configurations, compare performance, and generate executable SageMaker Python SDK v3 code.

10/4Sun
  1. Hugging Face Blog71

    Microsoft's ThinkingBox agent benchmark checks final database state, not tool calls

    The Microsoft Copilot Studio team and Hugging Face jointly released ThinkingBox. It runs agents in isolated MCP tool sessions and then performs executable checks on final backend state and side effects, rather than judging only the final reply or whether tool calls were allowed.

    Why it matters: The agent benchmark from Microsoft and Hugging Face uses 507 stateful workflows and 20 repeated runs, shifting the focus from tool calls to the final database state.

10/3Sat
  2. AWS Machine Learning Blog38

    Use Amazon Quick and the Adjudicated Query pattern to check thousands of leases for compliance

    AWS proposed the Adjudicated Query design pattern, in which business users ask compliance questions in the Amazon Quick chat interface while a deterministic rules engine makes the actual decision. An integrity receipt ensures that the sum of compliant, noncompliant, ambiguous, and unreadable records equals the total scanned, preventing records from being silently skipped. The pattern applies to high-risk cases such as lease compliance, sanctions screening, and insurance claims.

10/2Fri
  2. AWS Machine Learning Blog34

    Build ambient agents with Amazon Bedrock AgentCore, from event-driven signals to human-in-the-loop workflows

    Amazon Bedrock AgentCore supports building 'ambient agents' that listen to event streams such as Amazon S3 event notifications, EventBridge rules, and Lambda triggers and perform tasks automatically. When needed, they pause for human approval through a single ask_human tool and resume from where they stopped.

10/1Thu
9/30Wed
  1. AWS Machine Learning Blog22

    A practical guide to prompt engineering for Amazon Quick

    Prompt engineering determines how well Amazon Quick's AI features respond to natural-language requests, spanning custom agents, automated workflows, and conversational data analysis. The article presents general principles and the structured CRISPE framework, emphasizing specificity, business context, and examples. It uses enterprise scenarios such as customer-service efficiency analysis to show how to reduce iteration and improve first-pass accuracy.

  2. AWS Machine Learning Blog22

    Prompt engineering for Amazon Quick components: patterns and pitfalls

    AWS breaks down prompt-writing approaches for Amazon Quick components: Quick Research needs a clear objective, audience, and focus area, with sub-questions decomposed and data sources constrained; Quick Flows should specify timing, data sources, calculations, and delivery goals, using numbered steps for complex logic and conversational iteration for revisions; Quick Sight queries should state the business question, metrics, dimensions, and visualization preferences.

9/29Tue
  1. Hugging Face Blog32

    ProvenanceGuard: provenance-aware fact checking for MCP agents

    Hugging Face has published the ProvenanceGuard paper, describing a post-generation verification layer for MCP agents that preserves the identity of tool-output sources and detects cross-source confusion—claims that are true but attributed to the wrong source. On 281 real medical-agent traces, it blocked 138 of 139 claims experts said should be blocked, achieved about 86% source-identification accuracy, and scored highest among four comparison checkers.

9/28Mon
  1. Hugging Face Blog74

    H Company releases Holo4 agent models: 27B dense and 35B-A3B MoE versions

    H Company has released the Holo4 agent-model family, with a 27B dense model and a 35B-A3B MoE model. Both are available through the H Models API, and weights are open on Hugging Face in BF16, FP8, NVFP4, and 4-bit GGUF formats.

    Why it matters: Holo4 opens both its weights and complete evaluation traces, giving readers a basis to assess the cost and capability limits of general-purpose computer-use agents.

9/26Sat
9/25Fri
  1. GitHub Blog · AI & ML38

    GitHub Copilot's canvas: when chat is no longer the right AI interface

    The GitHub Copilot app has introduced canvas, a full-stack mini-app that runs inside the app without a browser shell, can communicate in both directions with the Copilot agent, and can call third-party APIs or run code locally. The company argues that chat is a general-purpose fallback, while a canvas can be a better fit when users have a specific task. For example, a SQLite canvas can operate on a database directly instead of having an agent repeatedly spend tokens on actions such as “stage and commit.”

  2. GitHub Blog · AI & ML63

    GitHub Security Lab releases Fuzzing Taskflow agent to automate the C/C++ fuzzing pipeline

    GitHub Security Lab has released Fuzzing Taskflow, an autonomous fuzzing pipeline for C/C++ projects. Given a GitHub repository URL, it identifies entry points, analyzes the build system, writes a harness, runs AFL++, reads coverage reports and improves the harness, triages crashes, and produces vulnerability reports.

    Why it matters: GitHub Security Lab hands harness writing, coverage analysis, and crash triage to an LLM agent, and has published the full implementation and instructions for running it.

9/18Fri
  1. GitHub Blog · AI & ML22

    GitHub Podcast unpacks AI hot takes: should you read code, is RAG dead, and did Skills kill MCP?

    The latest GitHub Podcast examines five popular AI claims. AI-generated code still needs to be read and owned, with review effort scaled to risk; Skills and MCP address different needs, with Skills packaging team expertise and MCP standardizing connections to tools and data; and RAG is not dead, since it can coexist with agents, Skills, and MCP in one workflow. The episode also discusses judging AI fluency in hiring and how fine-tuning needs can signal codebase maintainability problems.

9/16Wed
  2. Mistral AI60

    Mistral partners with Mozilla to power Firefox Smart Window

    Mistral has announced a partnership with Mozilla: the beta AI browsing assistant Firefox Smart Window is now powered by Mistral models. It helps users understand complex searches, find pages they previously visited, and organize information from browser tabs.

    Why it matters: Mistral and Mozilla are bringing open models into a browser entry point, offering a view of how open models may reach consumer users.

9/11Fri
9/10Thu
  2. OpenAI News60

    OpenAI releases Agents API

    OpenAI has released the Agents API, a hosted service powered by the Codex harness for building and launching agents in the cloud. It supports orchestration, long-running sessions, and tool calls.

    Why it matters: OpenAI is offering Codex's orchestration capabilities as a hosted service, giving readers a signal of how the barrier to deploying cloud agents may change.

9/3Thu
7/9Thu
  1. Mistral AI50

    Mistral Studio adds system-level versioning and governance for Prompts and Skills

    Mistral Studio now offers customers centralized management for Prompts and Skills, each with version history, clear ownership, and traceability. Published versions are immutable. The system supports version comparisons, rollbacks, classification tags, audit logs, and direct access through an MCP server. Assets can move from being visible only to their creator to a workspace or the entire organization; data remains within the enterprise boundary.

6/24Wed
  1. Mistral AI52

    Mistral AI adds admin controls, API-key scoping, and a debugger to Connectors

    Mistral AI has added several capabilities to Connectors. Admin controls are generally available, letting workspaces or organizations set access and toggle tools individually. Scoped API keys, also generally available, prevent automated AI workloads from impersonating a user. Multi-account connectors, also generally available, let one connector link multiple accounts.

5/28Thu
  2. Mistral AI62

    Mistral AI releases public preview of Search Toolkit

    Mistral AI has released the public preview of Search Toolkit, a composable framework for building production-grade search pipelines for AI applications. It is open-source and supports cloud, on-premises, and edge deployments. Ingestion, retrieval, and evaluation share one interface, with built-in BM25 sparse retrieval, dense-vector retrieval, and hybrid configurations, plus metrics such as recall, precision, MRR, and NDCG.

    Why it matters: Mistral brings ingestion, retrieval, and evaluation into one framework, offering a way to assess RAG retrieval quality separately.

5/22Fri
  1. Mistral AI62

    Mistral launches Connectors in Studio with built-in and custom MCP support

    Mistral has released Connectors in Studio as a public preview. Built-in and custom MCP connectors can be used through the API or SDK with all models and agent calls. New direct tool calls let developers decide when tools are invoked, and human-in-the-loop approval lets applications confirm before a tool executes.

    Why it matters: The source gives code paths for registering and calling connectors and for human approval, helping readers assess changes to enterprise tool integration.

4/27Mon
  1. Mistral AI71

    Mistral AI releases public preview of Workflows

    Mistral AI has released Workflows as a public preview of its enterprise AI orchestration layer. It offers persistent execution, observability, and fault tolerance to take AI processes from proof of concept into production. Organizations including ASML, ABANCA, CMA-CGM, France Travail, La Banque Postale, and Moeve already use it to automate key workflows.

    Why it matters: The source describes Workflows' persistent execution, observability, and human approval features, helping readers assess the orchestration requirements for enterprise AI deployments.