Model context protocol

Your Insecure MCP Server Won't Survive Production — Tun Shwe, Lenses

Your Insecure MCP Server Won't Survive Production — Tun Shwe, Lenses

Lenses.io experts Tun Shwe and Jeremy Frenay discuss the significant security and design hurdles in transitioning Model Context Protocol (MCP) servers from local development to enterprise production. They introduce five core principles for secure agentic design, including shrinking the attack surface and constraining inputs, and detail the necessity of remote MCP servers with robust authentication. The talk provides an in-depth comparison of OAuth 2.1's Dynamic Client Registration (DCR) and the more secure Client ID Metadata Document (CIMD) approaches for managing agent identities, offering a roadmap for building enterprise-grade agentic AI systems with MCP.

A2A vs MCP: AI Agent Communication Explained

A2A vs MCP: AI Agent Communication Explained

Discover how A2A (Agent2Agent) and MCP (Model Context Protocol) solve critical challenges in AI agent ecosystems. A2A enables seamless communication and collaboration between diverse AI agents, while MCP standardizes an agent's access to external tools and data, fostering robust and interoperable AI workflows.

Enterprise-ready MCP // Jiquan Ngiam

Enterprise-ready MCP // Jiquan Ngiam

Jiquan Ngiam, CEO of MintMCP, discusses the paradigm shift from static programs to dynamic AI agents, outlining the significant security risks involved—supply chain vulnerabilities, third-party data poisoning, and inadvertent agent behaviors—and presents a three-pronged strategy for enterprise readiness: comprehensive monitoring, preventative guardrails, and secure, role-based deployment of Model Context Protocols (MCPs).

Agentic Al in SW Development: Evolving Patterns & Protocols • Bhuvaneswari  Subramani • GOTO 2025

Agentic Al in SW Development: Evolving Patterns & Protocols • Bhuvaneswari Subramani • GOTO 2025

Bhuvaneswari Subramani details the "Agentic Shift" in AI by presenting an evolutionary journey through seven foundational system design patterns. The talk progresses from simple conversational clients to sophisticated, multi-agent systems, covering key patterns like Retrieval-Augmented Generation (RAG), Self-Correcting RAG, and the Model Context Protocol (MCP), explaining how each pattern adds new layers of context, action, and autonomy.

Production Ready AI Agents

Production Ready AI Agents

Sam Partee, CTO of Arcade, explains the critical gap between AI agents that gather context and those that take secure, real-world actions. He introduces Arcade as a middleware platform that solves complex challenges like user authorization, fine-grained permissions, and token management, enabling developers to build scalable, enterprise-ready agents.

Piloting agents in GitHub Copilot - Christopher Harrison, Microsoft

Piloting agents in GitHub Copilot - Christopher Harrison, Microsoft

GitHub's Christopher Harrison explains how to leverage GitHub Copilot's agent capabilities. This summary covers using Copilot as an AI pair programmer, the importance of providing context, its different workloads, and how to use the new Copilot Coding Agent with the Model Context Protocol (MCP) to accelerate development responsibly.