AI Agents

I wanted MCP auth to feel invisible. The dream was simple. User opens the app, starts chatting with the LLM. At some point they ask for something that needs an external tool. “What’s on my calendar this week?” The LLM realizes it needs Google Calendar access, prompts the user for permission right there, they approve, and the tool runs. Contextual. Just-in-time. No setup screens. This is how permissions should work. Ask for what you need, when you need it, and not a moment before. ...

When I started building Agentify, I took the straightforward path: hardcode the tool integrations directly into my Rails application. Need weather data? Write a method that calls the weather API. Need to search knowledge bases? Build it into the agent logic. It worked, but it wasn’t sustainable. Then the Model Context Protocol (MCP) standard emerged, promising a way to standardize tool interactions between AI agents and external services. Instead of building every integration myself, I could tap into a growing ecosystem of MCP servers. The question was: how do you actually implement this in a real Rails application? ...

What I Learned This Week 1. 12-Factor Agents This week I came across the 12-Factor Agents framework by HumanLayer, which adapts the well-known 12-Factor App principles for building robust and maintainable AI agents. Notable principle: Own Your Context Window Rather than sending the full conversation history with each agent interaction, this principle encourages summarizing or explicitly controlling the context that gets passed along. It’s a smart approach for performance and clarity — especially in longer or multi-agent flows. ...