Introduction to AI Agent Tooling, MCP, and Execution Environments

AI agents are only as powerful as the tools they can use and the environments in which they run. To build reliable agentic systems, you must understand how models securely interact with APIs, databases, and execution platforms. This text-based course guides you from foundational concepts to practical implementations of secure agent workflows. You will understand how to structure tool calls, implement the Model Context Protocol (MCP) to standardize agent-to-tool communication, and run agents safely within controlled environments like GitHub Actions. Through this course, you will: 1. Understand the core architecture of agentic workflows and tool-use patterns. 2. Configure the Model Context Protocol to establish standard client-server connections for AI models. 3. Build secure execution boundaries to protect system resources during agent runtime. 4. Deploy agent workflows using GitHub Actions for automated, event-driven execution. 5. Apply governance and security best practices to prevent unauthorized tool access and prompt injection exploits. The course begins with essential definitions of tools, schemas, and execution sandboxes before moving into step-by-step written walkthroughs of MCP configuration and GitHub workflow setup. You will read detailed code explanations and complete practical written exercises to cement your understanding of secure agent operations. Designed for software developers, system architects, and AI enthusiasts new to agentic engineering, this course requires only basic programming knowledge. Start reading today to master the infrastructure behind modern, secure AI agents.