Guides, tools, and templates for building AI agents that hold up in production.
Production AI agents — memory, orchestration, tooling, and more
Built by Ibrahim
Most AI agent tutorials stop at the demo. Getting to production means solving for memory, orchestration, auth, streaming, and the right tool-connectivity layer — whether that's MCP, a REST API, or something custom. That's what Agentailor covers.
30+ articles on agent memory, orchestration, fullstack deployment, MCP, LangGraph, and more. Real experiments, honest lessons.
Explore →create-mcp-server, slimcontext, and more to accelerate development.
View on GitHub →Ready-to-deploy agent starters with human-in-the-loop, memory, tool integrations, and cloud deployment configs.
Browse →From agent architecture to fullstack deployment
I'm Ibrahim — I help developers build AI agents that actually work in production.
I work across the full stack of production AI systems — agent memory and orchestration, fullstack TypeScript architecture, tool connectivity (including MCP), and cloud deployment. Everything I build or write is oriented toward closing the gap between demos and real-world reliability.
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