Inside Agentic Infrastructure: Building Scalable AI Systems

Описание: 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

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