Engineering
Metorial blog posts about engineering.
AI infrastructure accumulates
There is no meeting where someone decides to build an internal AI platform. It just accretes, one reasonable decision at a time, until eighteen months later you have thirty-five thousand lines of glue code, one person who understands it, and no documentation. Here is how that happens, and why the fix is not writing better glue.
Karim RahmeHow to Architect, Deploy, and Operate Production-Grade AI Agents
Moving AI agents from a promising pilot to a production-grade system is a monumental challenge, with a staggering 95% of projects failing to deliver value. The secret to success lies not in the AI models themselves, but in a robust architecture focused on scalability, observability, and security. This article explores the core principles for building and operating reliable AI agents, highlighting the critical role of the Model Context Protocol (MCP) and how platforms like Metorialprovide the serverless infrastructure needed to bridge the gap from experimentation to enterprise-scale deployment.
MetorialHow to build and deploy a Model Context Protocol (MCP) server
The Model Context Protocol (MCP) is an open standard that’s revolutionizing how AI agents connect to external tools and data. While building a basic MCP server is straightforward, deploying, scaling, and securing it for production is a significant challenge. Metorial’s serverless MCP runtime simplifies this entire process, allowing developers to deploy robust, scalable MCP servers in just a few clicks, so they can focus on building innovative AI applications instead of managing infrastructure.
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