Inside Agentic Infrastructure: Building Scalable AI Systems

Description

In this video, I break down an Agentic Infrastructure MVP I’ve been working on — a modular AI architecture that scales reasoning across multiple domain agents. You’ll see how an Orchestrator (GPT-4) coordinates with an Agent Registry to dynamically discover and route tasks to specialized agents — each with their own Model Communication Protocol (MCP) layer, written in Python and hosted in the cloud. We’ll walk through the end-to-end flow: • User → Orchestrator → Agent Registry → Agent → MCP → Tools • How the orchestrator handles reasoning, memory, and planning • How agents register, communicate, and execute via JSON-RPC • Why the architecture uses a heavy-vs-light model design for flexibility • Observability, telemetry, and security design choices I’ll also show a quick look at Promptify Studio — a side project I built to help people enhance and understand prompts interactively. Whether you’re an AI engineer, DevOps architect, or just an enthusiast, this walkthrough is a practical look into how multi-agent AI systems can be built in the real world. ⚙️ Technologies used: Python · Azure App Services · GPT-4 · JSON-RPC · Terraform · Firebase · Flask 🌐 Projects mentioned: • Agentic Infrastructure MVP • Promptify Studio: https://promptify.studio/ — prompt enhancement & education tool 🔔 Subscribe for upcoming videos on asynchronous orchestration, RAG integration, and model evaluation pipelines.