moshebeeri

Agentic Provider-Consumer Pattern

Agent-to-Agent Communication using A2A-MCP protocols

Introduction

My journey into building scalable agentic systems began with a simple challenge: I needed a framework that could handle complex workflows with multiple AI agents working together seamlessly. Initially, I explored the Motia framework, which provided an excellent foundation for orchestrating event-driven workflows with zero infrastructure setup. The code-first approach and built-in observability were exactly what I needed for rapid development.

However, as my agent ecosystem grew more complex, I discovered the need for more dynamic context sharing between agents. This led me to the Model Context Protocol (MCP), which offered a standardized way to provide resources and prompt templates to AI models. I realized I could create a catalog of MCP resources that would automate the creation of specialized agents, each with their own unique capabilities but speaking a common language.

The final piece of the

moshebeeri

from openai import OpenAI
import os

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])


def get_completion(messages, model="gpt-4", temperature=0.7):
    response = client.chat.completions.create(model=model,
                                              messages=messages,
                                              temperature=temperature)