Multi-Agent Systems
AGAI 301
Explore the design and behavior of systems with multiple collaborating AI agents. Learn how agents communicate, coordinate, divide labor, and resolve conflicts — and how emergent behaviors arise when many agents interact.
Beyond the Single Agent
A single agent can accomplish a great deal. But many important tasks benefit from — or require — multiple agents working in concert. Multi-agent systems divide complex work across specialized agents, enable parallelism, allow peer review, and can exhibit emergent capabilities beyond what any single agent achieves alone.
Coordination and Emergence
The most fascinating and challenging aspects of multi-agent systems are coordination and emergence. How do agents communicate? How do they avoid duplicating work or contradicting each other? And what behaviors arise when many agents interact that cannot be predicted from studying any agent in isolation?
What You Will Learn
You will survey hierarchical, peer-to-peer, and market-based multi-agent architectures; implement a basic multi-agent pipeline; and analyze how emergent behaviors arise from agent interactions. You will also explore debate and adversarial agent patterns, learn how to evaluate multi-agent systems for correctness and reliability, and identify safety risks specific to systems where multiple agents share authority and resources.
Who This Course Is For
This course is for engineers and researchers who are designing AI systems where a single agent is not enough — whether because of task complexity, specialization requirements, parallelism needs, or quality verification demands. Solid understanding of single-agent architectures is required. This course is where the curriculum shifts from individual agents to coordinated AI systems.
What you will learn
- Describe the main multi-agent architectural patterns
- Implement a basic multi-agent pipeline
- Explain how emergent behaviors arise from agent interactions
- Design coordination mechanisms for collaborative agents
- Identify safety risks specific to multi-agent systems
- Evaluate multi-agent systems for correctness and reliability
Major topics
Why this course matters
As AI systems take on more complex tasks, multi-agent architectures are becoming the standard for production AI pipelines. Understanding how to design and evaluate these systems is essential for building reliable AI at scale.
Course modules
Multi-Agent Architecture Patterns
Learn the major ways multi-agent systems are organized, including hierarchical teams, peer-to-peer collaboration, pipelines, and market-style delegation. This module also covers communication protocols and role specialization so agents can work together without becoming chaotic.
Coordination, Consensus, and Emergent Behavior
Explore how multiple agents coordinate decisions, resolve disagreements, and produce higher-quality outputs through debate, critique, and consensus. This module also explains emergent behavior and why multi-agent systems can become unpredictable without strong controls.
Evaluation, Testing, and Safety
Learn how to evaluate multi-agent systems as systems rather than isolated model calls. This module covers trajectory testing, simulation, safety risks, prompt injection, collusion, runaway loops, and deployment guardrails.
Common misconceptions
Multi-agent systems are just multiple chatbots running in parallel
More agents always produce better results
Emergent behavior is always beneficial
Multi-agent systems are too complex for production use
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