OSS interview-prep repo for AI, agent architectures and tool-calling (github.com)

A comprehensive open-source repository, ai-agent-tool-calling, bundles tutorials, working examples, architecture diagrams and interview prep to teach developers how to build production-ready AI agents that call external tools, APIs, databases and system commands. It addresses a core LLM limitation—models can’t access real-time data or execute actions—by teaching tool-calling patterns, protocols (UTCP and MCP), and agent architectures (reactive agents, planner-executor, multi-agent systems). The repo includes end-to-end projects (data analyst bot, DevOps copilot, customer support assistant), language examples (Python, TypeScript, LangChain integrations), utility scripts (mock API server, tool tracer), and interview questions/solutions to help engineers prepare for system-design and implementation interviews. Technically significant for the AI/ML community, the resource compares UTCP (direct, stateless, lower latency, smaller attack surface) and MCP (client-server, stateful, centralized control) and provides security best practices and risk trade-offs for production deployments. It supplies runnable code (Python 3.10+, OpenAI/local LLM examples), protocol deep dives, design patterns and anti-patterns, and guidance on reliability and safe tool exposure—making it a practical bridge from research demos to deployable agent systems. The project is MIT-licensed, community-driven, and intended as an educational hub for engineers, researchers and technical leaders evaluating tool-calling standards.