Before you build an AI agent, you need to know when an agent is the right solution and how the software that powers it actually works. This course gives you both. You'll learn to distinguish agentic architectures from traditional prompt-response patterns, set up a GitHub Copilot SDK development environment, build your first working agent, and analyze the SDK's internal runtime so you can diagnose problems with confidence.
You'll start by evaluating real-world tasks for agentic automation, weighing decision complexity, tool requirements, and multi-step structure. From there, you'll configure the Copilot SDK and CLI across Python and Node.js, then build a single-task agent that accepts a prompt, plans its steps, and returns structured output using the SDK's built-in agentic loop. You'll finish by tracing the JSON-RPC communication between your application, the SDK client, and the Copilot CLI server.
The course closes with a hands-on project: a feasibility assessment of enterprise tasks paired with a proof-of-concept agent that validates your recommendation.
Applied Learning Project
Every long course culminates in a hands-on project that produces a portfolio-ready artifact you can show employers and discuss in interviews. The projects build progressively, mirroring real enterprise development work.
You start by writing a feasibility assessment recommending where agentic automation fits, validated with a working proof-of-concept agent. Next, you build a multi-turn conversational agent that remembers context, then a tool-integrated agent that reaches external data through MCP servers. You then embed a multi-agent system into a working application with model routing, and finish with a production-ready capstone: a complete agent system with automated tests, observability, and security controls.
Each project tackles an authentic problem the way a working AI engineer would, giving you concrete, demonstrable work samples for your resume and interviews — not just a certificate of completion.
