Multi-Agent Collaboration & Orchestration
Move from a single agent to a multi-agent system: learn to design agent roles, task allocation, delegation, communication, shared context, handoffs, conflict resolution and orchestration, decide when multiple agents are worth it, avoid overlapping responsibilities and uncontrolled information, and complete your own Multi-Agent Architecture Canvas.
Course storyline: how multiple agents complete a shared goal
The whole course follows one line: decompose, assign, delegate, communicate, coordinate, resolve, orchestrate, evaluate.
- 1. Decompose
What work must be done?
- 2. Assign
Which agent should own which responsibility?
- 3. Delegate
How is work passed from one agent to another?
- 4. Communicate
What information must agents exchange?
- 5. Coordinate
How do agents align timing, dependencies and shared goals?
- 6. Resolve
What happens when agents disagree or duplicate work?
- 7. Orchestrate
How is the overall system controlled?
- 8. Evaluate
Did collaboration improve the outcome?
Your next step
AI 301 · Module 1: From Single Agent to Multi-Agent System
Curriculum
- Module 1From Single Agent to Multi-Agent SystemWhen is one agent not enough, and when does multi-agent only add complexity?
- Module 2Role Design & Responsibility BoundariesHow should agents divide the work so they do not all do the same thing?
- Module 3Task Allocation, Delegation & HandoffWhich agent should own the next task, and how is work handed over safely?
- Module 4Agent Communication & Message ContractsWhat should agents exchange, and how do you avoid lots of chatter but poor collaboration?
- Module 5Shared Context, Memory & CoordinationShould agents share all information, or should each see only the context it needs?
- Module 6Orchestration Patterns & Collaboration TopologiesWho coordinates the whole multi-agent system?
- Module 7Conflict Resolution, Human Oversight & Multi-Agent EvaluationWhat does the system do when agents disagree, duplicate work or conflict?
- Module 8Multi-Agent Architecture CanvasHow do you turn an agent team into an implementable multi-agent architecture?
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Practice and assessment
- Activity 1: One Agent or Many?Judge whether a use case really needs a multi-agent architecture.Not done yet
- Activity 2: Design the Agent TeamDesign 3 to 5 clear agent roles with responsibility boundaries.Not done yet
- Activity 3: Delegate or Keep?Decide whether a task stays with the current agent, is delegated, is parallelized or goes to a human.Not done yet
- Activity 4: Agent Communication ContractDesign an agent-to-agent message contract.Not done yet
- Activity 5: Shared or Private?Design shared context, private context, knowledge and restricted information.Not done yet
- Activity 6: Choose the Orchestration PatternCompare Supervisor, Planner/Executor, Specialist, Peer-to-Peer, Hierarchical and Hybrid.Not done yet
- Activity 7: Resolve the Multi-Agent ConflictHandle conflicts between agents over responsibility, evidence and decisions.Not done yet
- AI 301 assessmentTwo levels: knowledge checks and collaboration design scenarios.Not done yet
- Multi-Agent Architecture CanvasBring roles, delegation, communication, context, orchestration and evaluation together in a Multi-Agent Architecture Canvas you can take back to work, research or an industry project.Not done yet
About this course
Outcomes
- Decide when to use a single agent and when a multi-agent architecture is justified
- Design agent roles, capabilities, permissions and responsibility boundaries
- Design task allocation, delegation and handoff
- Define an agent-to-agent communication contract
- Distinguish shared context, private context and shared knowledge
- Compare several orchestration patterns
- Design conflict resolution, human escalation and multi-agent evaluation
- Complete a Multi-Agent Architecture Canvas
Prerequisites
- AI 202 is recommended, or a basic grasp of agent planning, reasoning, state, action and adaptive planning
- Understanding the tool, MCP and permission concepts of AI 201 is recommended
- The context, memory, knowledge and skills concepts from AI 103 make shared and private context easier to follow
- No distributed-systems or advanced programming background required
- The course focuses on multi-agent architecture design, not on any framework API
Who it is for
- Learners who finished AI 202 and are ready to move from a single agent to a multi-agent system
- Coordinators and managers responsible for AI agents, AI copilots, intelligent processes, cross-department automation or digital transformation
- Students and researchers who want to design agent teams with specialization, independent verification or permission separation
- Learners preparing to study agent harnesses and reliable automation next
- Teams that need to apply agentic AI to real industry-academia projects
We suggest finishing AI 202 and understanding the tool, MCP and permission ideas of AI 201. This is a suggestion only and never blocks you from starting.
Certificate
Earns a course completion certificate (L1); a capability certificate (L2) is also available through additional artifact review.
Requirements to earn it
- Complete all 8 course modules
- Pass the post-test assessment at 80% or higher
- Submit a project artifact and pass review
Course feedback
It takes 2 to 3 minutes and is used to improve the course.