A team built an MCP bridge to let their cloud-hosted AI agent on Amazon Bedrock access local MCP tools running on users' machines, solving the gap between remote AI clients and local servers. The internal system has handled over 41,000 conversations in a year, using WebSocket tunneling between the cloud agent and a local FastMCP proxy that communicates with MCP servers via stdio. Users can now run centrally deployed AI agents that act on local files like Excel spreadsheets while drawing context from their browser, with no credentials leaving their machine.
Amazon Web Services released an open-source n8n community node that integrates Bedrock AgentCore, enabling users to build production AI agents with persistent memory, tool access, and multi-model support directly in n8n's visual editor without writing infrastructure code.Node version 0.3 is now available and works with Amazon Bedrock, OpenAI, Google Gemini, and LiteLLM-supported providers, with agents running in isolated sessions and supporting features like code interpretation, skills from AWS catalogs, and private VPC deployment.Teams can now deploy agents with managed memory, real tools like code sandboxes and web browsers, and model flexibility without building the orchestration layer themselves, reducing the infrastructure work required for production AI systems.
An article updates previous lessons on building with AI, focusing on managing AI agents that can now perform longer, more complex tasks autonomously. In May 2025, roughly 25% of Codex users requested agent work equivalent to eight human work hours per month, up from 2% in December 2024. The piece provides guidance on structuring agent tasks through clear finish lines, strategic model selection, and measuring output quality rather than token consumption.
DoorDash released a command-line interface (CLI) in July 2026 that allows AI agents to autonomously place food orders without human confirmation, departing from the company's existing in-app AI assistants that require user validation. The CLI is gated behind a macOS-only beta with a waitlist and Google Form intake, restricting access to a few thousand developers rather than broad consumers. This move signals DoorDash's shift toward building agent-native infrastructure rather than consumer-facing features, effectively positioning itself as a platform for autonomous commerce rather than relying on the home-screen habit that currently drives its ad business.
AI workflow automation platforms are increasingly targeting physical and science sectors like materials discovery and drug development, moving beyond digital-only applications. European advanced materials startups raised €3bn this year versus €1.6bn last year, with drug discovery startups on pace to match €4.7bn annual funding as AI adoption accelerates. Success requires embedding physics constraints into AI models, deep domain expertise, and redesigning workflows from the ground up rather than adding AI as an afterthought to existing processes.
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