What is multi-agent orchestration in this course?

In this course, multi-agent orchestration means designing a system where specialized agents divide, hand off, and complete parts of a larger task together. The emphasis is on structuring delegation, shared state, and coordination so the system behaves reliably instead of acting like one oversized agent.

When would you use multi-agent orchestration?

You would use multi-agent orchestration when one request is too broad, fragile, or multi-step for a single agent to handle cleanly. The course focuses on situations where work needs to be split across planning, execution, or specialist roles without losing coordination.

How does multi-agent orchestration fit into a broader workflow?

It sits between defining individual agents and running a dependable end-to-end service. In this course, orchestration is the layer that connects planning, delegation, state sharing, and monitoring into a repeatable process.

How is multi-agent orchestration different from using one general-purpose agent?

A single-agent setup keeps all reasoning and tool use inside one general-purpose agent. Multi-agent orchestration separates responsibilities across specialists and adds routing and handoff rules so complex work can be managed more deliberately.

Do you need any prerequisites before learning multi-agent orchestration?

A basic understanding of Python and how an agent can use tools is helpful before learning multi-agent orchestration. What matters most is being comfortable following multi-step logic, typed inputs, and the flow of information between coordinated components.

What tools, platforms, or methods are used in this course?

The course centers on the OpenAI Agents SDK, with a focus on typed tool design and planner-executor or handoff-based orchestration patterns.

What specific tasks will you practice or complete in this course?

You practice defining type-safe tools, routing work between specialized agents, and preserving shared context during handoffs. You also build planner-executor flows and add tracing or error-handling patterns so coordinated systems are easier to inspect and maintain.

Multi-Agent Systems and Orchestration

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Multi-Agent Systems and Orchestration

This course is part of Build Powerful AI Agents with OpenAI Tools Professional Certificate

Instructor: Professionals from the Industry

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4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

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This course is part of the Build Powerful AI Agents with OpenAI Tools Professional Certificate

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There are 4 modules in this course

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.

Module details

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.

What's included

3 videos4 readings3 assignments3 ungraded labs

3 videosTotal 10 minutes

1.1 The Abstraction Ladder4 minutes
1.5 The Anatomy of a Handoff3 minutes
1.11 Type Safety as Security3 minutes

4 readingsTotal 40 minutes

1.2 The Architecture Shift10 minutes
1.6 Choreography vs. Orchestration10 minutes
1.9 Spec-Driven Development10 minutes
1.12 Type Hints as Contracts10 minutes

3 assignmentsTotal 120 minutes

1.4 Migration Pains30 minutes
1.10 Context Detective30 minutes
1.14 Module 1 Assessment60 minutes

3 ungraded labsTotal 80 minutes

1.3 The Cleanup Job30 minutes
1.8 The Broken Switchboard25 minutes
1.13 The Negative Refund25 minutes

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.

What's included

2 videos3 readings2 assignments2 ungraded labs

2 videosTotal 9 minutes

2.1 Separating Planning from Execution4 minutes
2.5 Debugging by Tracing Execution4 minutes

3 readingsTotal 40 minutes

2.2 Reasoning Patterns10 minutes
2.6 Tracing & Spans10 minutes
2.9 The Scale-Up with SDD20 minutes

2 assignmentsTotal 70 minutes

2.4 Plan Auditing10 minutes
2.10 Module 2 Assessment60 minutes

2 ungraded labsTotal 60 minutes

2.3 The Planner-Executor Build30 minutes
2.7 The Optimization30 minutes

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.

What's included

3 videos4 readings3 assignments1 programming assignment3 ungraded labs

3 videosTotal 12 minutes

3.1 The Amnesiac Agent5 minutes
3.5 Agents that Remember4 minutes
3.10 The Needle in the Haystack3 minutes

4 readingsTotal 52 minutes

3.2 Shared Context Patterns15 minutes
3.6 Embeddings 101 for Agents15 minutes
3.8 Spec-Driven Knowledge Base10 minutes
3.11 Recency vs. Relevance12 minutes

3 assignmentsTotal 120 minutes

3.4 Race Conditions30 minutes
3.9 Vector Forensics30 minutes
3.15 State & Memory Architecture60 minutes

1 programming assignmentTotal 60 minutes

3.13 The Hybrid Memory Manager60 minutes

3 ungraded labsTotal 105 minutes

3.3 The Persistent Agent30 minutes
3.7 Building the Vault45 minutes
3.12 The Context Window Manager30 minutes

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.