Overview

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Hard problems break single agents. This course shows how specialized agents collaborate and operate in real applications at enterprise scale using the GitHub Copilot SDK. You'll route tasks across models by complexity, cost, and latency; design multi-agent architectures with collaborating sub-agents; embed the SDK into an application with event-driven triggers; and validate workflows through integration testing. By the end, you'll be able to: - Implement model routing by task complexity, cost, and latency - Design multi-agent architectures with specialized sub-agents - Embed the Copilot SDK into apps with event-driven triggers - Validate domain workflows through integration testing You'll use the GitHub Copilot SDK (Python and Node.js/TypeScript), MCP servers, the GitHub Copilot CLI, VS Code, and Git/GitHub.

Syllabus

Multi-Model Routing & Selection
- Implement model selection logic that routes agent tasks to the right AI model based on task complexity, cost, and latency. You'll learn to configure multi-model sessions, define routing rules, and benchmark model performance to make informed selection decisions, essential for building cost-effective, performant agent systems at scale.
Designing Multi-Agent Workflows
- Design multi-agent architectures where specialized sub-agents collaborate on complex tasks. You'll learn to implement researcher/editor and other collaboration patterns, manage inter-agent communication, and orchestrate workflows where agents with different capabilities work together; the key pattern for building enterprise-scale agent systems. This is the most architecturally complex short course in the program — multi-agent systems are the pattern for handling task complexity that overwhelms a single agent.
Embedding Agents into Applications
- Embed the Copilot SDK into an existing application with event-driven triggers, domain-specific tools, and user-facing interaction surfaces. You'll learn the integration patterns that transform standalone agent scripts into embedded application features, moving from "agent as a tool" to "agent as a product capability."
Domain Workflow Design & Integration Testing
- Design domain-specific agent workflows and validate them through integration testing. You'll learn to define end-to-end agent workflows for specific business domains, write integration tests that verify tool invocations, session behavior, and output quality, and build confidence that embedded agents behave correctly before production deployment.
GenAI Module_AI-Assisted Multi-Agent Design
- Explore how generative AI tools can accelerate multi-agent system design, workflow specification, and integration test generation. You'll learn to use AI as a design accelerator for complex architectural tasks while critically evaluating AI-generated architectures and test suites.
Project Module_Embedded Multi-Agent Application
- Build an embedded multi-agent application that combines model routing, multi-agent orchestration, application integration, and integration testing into a comprehensive portfolio artifact. This is the most complex project in the program, combining multi-model routing, multi-agent orchestration, application embedding, and integration testing into one cohesive deliverable.