Overview

The Multi-Agent Systems and Orchestration course teaches learners how to design and coordinate AI agents that work together as collaborative systems. Starting with the OpenAI Agents SDK, participants explore how to structure planner–executor architectures, enabling agents to break down complex tasks into coordinated subtasks. The course emphasizes orchestration strategies such as multi-agent collaboration, workflow delegation, and state sharing across agents, supported by design principles for efficiency and reliability. Learners also examine observability and monitoring techniques to track agent decisions, as well as fault tolerance strategies to handle errors gracefully in production settings. Advanced modules introduce hybrid human–agent workflows, parallel execution patterns, and enterprise-level orchestration for scalability. Through hands-on labs and guided projects, learners will build an Automated Research Team, demonstrating how multiple agents can gather information, analyze data, and synthesize findings. By course completion, participants will have the skills to design multi-agent systems that deliver scalable, reliable, and coordinated AI-driven solutions.

Syllabus

Agents SDK Fundamentals

In this module, you'll join LegacyCorp as an AI Consultant, tasked with modernizing their internal logistics and support systems. You will learn to transition from brittle, manual execution loops to resilient architectures using the Agents SDK (Software Development Kit), mastering key concepts like Orchestration, Handoff Hooks, and Type-Driven Design. Through a series of forensic labs and design challenges, you will build a scalable "Hub-and-Spoke" system capable of managing specialized agents and securing critical tools against misuse.

Advanced Orchestration Patterns

In this module, you will step into the role of Lead Architect at Praxis AI to tackle complex orchestration challenges for two distinct clients. First, you will rescue the Urban Hop travel assistant by implementing the Planner-Executor pattern, separating high-level reasoning from deterministic execution to ensure reliability. Next, you will transition to a Site Reliability Engineer (SRE) role for Global Freight, using distributed tracing and observability to diagnose and fix race conditions in a high-concurrency logistics engine. By the end of the module, you will have mastered the architectural patterns necessary to build agentic systems that are not just intelligent, but predictable, scalable, and debuggable.

Distributed State & Long-Term Memory

In this module, you tackle the complexity of persistent memory in distributed systems. You will act as a Systems Engineer for Global Freight to solve critical "Lost Update" race conditions using Redis and pessimistic locking. Simultaneously, you will serve as an AI Architect for Urban Hop, implementing Vector Stores (for use with Retrieval Augmented Generation, also known as RAG) and memory optimization strategies to give your agent long-term, semantic recall without blowing up the context window.

Production Systems

In this final module, we shift focus from functionality to viability. You will transition from an AI Architect to a Site Reliability Engineer (SRE) for Global Freight Co., addressing critical issues in a live staging environment. The system is currently technically operational but commercially unviable due to severe security vulnerabilities (PII leaks) and inefficient resource usage (high costs and latency). You will first secure the system using the Agents SDK's native guardrails to intercept and redact sensitive data. Then, you will optimize performance by implementing caching strategies and model orchestration to reduce latency and costs, ensuring the system is ready for production deployment.