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Building AI Agents with MCP: Architecture, Security, and Enterprise Deployment

Webrix
Webrix

A comprehensive guide to how AI agents work, how MCP connects them to your tools, and what you need to know to deploy them safely at scale—from agent architecture to enterprise security patterns.

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The 10 MCP Security Risks Enterprise Teams Are Underestimating
SecurityNovember 26, 2025

The 10 MCP Security Risks Enterprise Teams Are Underestimating

Idan Chetrit
Idan Chetrit

MCP shipped without built-in authentication, and its design delegates all security enforcement to implementers. Six critical CVEs in the first year, 43% of servers vulnerable to command injection—here are the ten risks your security team needs to understand.

MCP Apps Extension: Why Interactive UI Matters for Enterprise AI Agents
Model Context ProtocolNovember 24, 2025

MCP Apps Extension: Why Interactive UI Matters for Enterprise AI Agents

Eyal Ben Ezra
Eyal Ben Ezra

The MCP Apps Extension standardizes interactive UIs for MCP servers. Why this matters for enterprise use cases like approvals, data visualization, and compliance.

Too Many Tools: Surviving MCP Tool Overload
IndustryNovember 10, 2025

Too Many Tools: Surviving MCP Tool Overload

Webrix
Webrix

At the MCP Dev Summit in London, Webrix CEO Shalev Shalit revealed why giving your AI agent access to hundreds of tools creates costly chaos—and introduced the Dynamic MCP approach that's changing how enterprises manage tool access.

Before You Build Your Next MCP: Think Like a PM
ProductNovember 10, 2025

Before You Build Your Next MCP: Think Like a PM

Eyal Ben Ezra
Eyal Ben Ezra

Great engineering teams build technically perfect MCPs that nobody uses. The difference between technical completeness and actual adoption comes down to one thing: thinking jobs-to-be-done over capabilities.

The 4 New MCP Superpowers Changing Developer Experience in Cursor
TechnicalNovember 01, 2025

The 4 New MCP Superpowers Changing Developer Experience in Cursor

Eyal Ben Ezra
Eyal Ben Ezra

Cursor's latest MCP implementation introduces four features that fundamentally change how AI coding assistants access context: Prompts, Resources, Elicitation, and Dynamic Tools. Here's what they mean for your development workflow.

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