AI_agent

I’m currently working on an ongoing online course and project where I’m developing my own AI agent, using n8n as an automation workflow tool to streamline tasks and decision-making. As part of this journey, I’ve explored building systems that allow AI to interact with code repositories, like in here , an open-source Python implementation of a Model Context Protocol (MCP) server. This server lets an AI assistant explore and manage a GitHub repository programmatically—listing files, reading contents, checking repository stats, and even creating issues through the GitHub API. It acts as a bridge between an AI agent and GitHub, processing JSON-based requests and returning structured results, enabling richer, automated repository interactions.

Alongside this, I’ve been experimenting with Multi-Agent Task Management System (here) , a Python project that demonstrates autonomous collaboration and task management between software agents. It includes modules for different agent roles, inter-agent communication, task queues, and logging, simulating how agents can perceive, decide, and act within a coordinated framework. The ultimate goal of these efforts is to build a multi-agent system for a business model, where each agent performs specialized tasks in pulling data, analyzing information, and supporting decision-making processes to drive effective business outcomes.