ai-agent-deep-dive-teaching-framework

An AI agent core loop processes inputs, queries an LLM, and triggers actions. This teaching framework demonstrates the mechanism by running a swappable interface to show how modular inputs map to agent actions.

You can build a teaching framework for AI agents in Python by using this minimal environment, which requires Poetry for dependency management and a designated directory to host pluggable skills for classroom experiments.

Yes, you can swap a fake teaching LLM for production models. The framework provides a swappable LLM interface to switch between classroom testing and production-grade querying within the core agent loop.

You load pluggable skills by placing them in a skills directory. The agent features skills discovery to automatically load and run modular components from the directory via its command-line interface.

No, this AI agent framework is a teaching-focused environment designed for classrooms and developers exploring core agent loops. It uses a swappable LLM interface for experiments rather than production-scale deployment.