Understand agent orchestration
The Microsoft Agent Framework SDK's agent orchestration framework makes it possible to design, manage, and scale complex multi-agent workflows without having to manually handle the details of agent coordination. Instead of relying on a single agent to manage every aspect of a task, you can combine multiple specialized agents. Each agent with a unique role or area of expertise can collaborate to create systems that are more robust, adaptive, and capable of solving real-world problems collaboratively.
By orchestrating agents together, you can take on tasks that would be too complex for a single agent—from running parallel analyses, to building multi-stage processing pipelines, to managing dynamic, context-driven handoffs between experts.
Why multi-agent orchestration matters
Single-agent systems are often limited in scope, constrained by one set of instructions or a single model prompt. Multi-agent orchestration addresses this limitation by allowing you to:
- Assign distinct skills, responsibilities, or perspectives to each agent.
- Combine outputs from multiple agents to improve decision-making and accuracy.
- Coordinate steps in a workflow so each agent’s work builds on the last.
- Dynamically route control between agents based on context or rules.
This approach opens the door to more flexible, efficient, and scalable solutions, especially for real-world applications that require collaboration, specialization, or redundancy.
Understand workflows in the Microsoft Agent Framework
The Microsoft Agent Framework provides workflows — structured sequences of steps used to complete a task. These workflows can include one or more AI agents alongside other components to automate complex operations.
Workflows give developers control over how tasks are executed, enable multi-agent orchestration, and support checkpointing to save and resume workflow states.
Core Components of a Workflow
Executors
Executors are the main workers in a workflow. They receive input messages, perform specific actions, and produce outputs that move the workflow toward completing its goal.
Executors can represent AI agents or custom logic components.
Example: One executor could analyze a travel request, while another books the flight or hotel based on the results.
Edges
Edges define how messages flow between executors, determining the logic and order of execution. The Microsoft Agent Framework supports several types of edges:
Direct Edges: Connect one executor directly to another in sequence.
Example: After an AI agent gathers user input, the next executor processes the booking.*Conditional Edges: Trigger only when certain conditions are met.
Example: If hotel rooms are unavailable, the workflow branches to an executor that suggests alternative dates or locations.*Switch-Case Edges: Route messages to different executors based on predefined conditions.
Example: VIP customers might be routed to a premium service executor, while others follow the standard process.*Fan-Out Edges: Send a single message to multiple executors simultaneously.
Example: One request could be sent to several agents — one checking flights, another checking hotels.*Fan-In Edges: Combine multiple messages from different executors into one for a final step.
Example: After gathering hotel and flight results, a summary executor compiles them into a single travel itinerary.*
Events
The Microsoft Agent Framework includes built-in events to improve observability and debugging during workflow execution. These events help developers monitor progress, track errors, and analyze system performance.
| Event Name | Description |
|---|---|
| WorkflowStartedEvent | Triggered when workflow execution begins. |
| WorkflowOutputEvent | Emitted when the workflow produces an output. |
| WorkflowErrorEvent | Occurs when an error is encountered. |
| ExecutorInvokeEvent | Fired when an executor starts processing a task. |
| ExecutorCompleteEvent | Fired when an executor finishes its work. |
| RequestInfoEvent | Logged when an external request is issued. |
Workflows in the Microsoft Agent Framework allow developers to design, monitor, and control how multiple AI agents and logic components interact to complete complex tasks. They bring structure, flexibility, and transparency to agent-driven applications.
Supported orchestration patterns
Microsoft Agent Framework provides several orchestration patterns directly in the SDK, each offering a different approach to coordinating agents. These patterns are designed to be technology-agnostic so you can adapt them to your own domain and integrate them into your existing systems.
- Concurrent orchestration - Broadcast the same task to multiple agents at once and collect their results independently. Useful for parallel analysis, independent subtasks, or ensemble decision making.
- Sequential orchestration - Pass the output from one agent to the next in a fixed order. Ideal for step-by-step workflows, pipelines, and progressive refinement.
- Handoff orchestration - Dynamically transfer control between agents based on context or rules. Great for escalation, fallback, and expert routing where one agent works at a time.
- Group chat orchestration - Coordinate a shared conversation among multiple agents (and optionally a human), managed by a chat manager that chooses who speaks next. Best for brainstorming, collaborative problem solving, and building consensus.
- Magentic orchestration - A manager-driven approach that plans, delegates, and adapts across specialized agents. Suited to complex, open-ended problems where the solution path evolves.
A unified orchestration workflow
Regardless of which orchestration pattern you choose, the Microsoft Agent Framework SDK provides a consistent, developer-friendly interface for building and running them. The typical flow looks like this:
- Define your agents and describe their capabilities.
- Select and create an orchestration pattern, optionally adding a manager agent if needed.
- Optionally configure callbacks or transforms for custom input and output handling.
- Start a runtime to manage execution.
- Invoke the orchestration with your task.
- Retrieve results in an asynchronous, nonblocking way.
Because all patterns share the same core interface, you can easily experiment with different orchestration strategies without rewriting agent logic or learning new APIs. The SDK abstracts the complexity of agent communication, coordination, and result aggregation so you can focus on designing workflows that deliver results.
Multi-agent orchestration in the Microsoft Agent Framework SDK provides a flexible, scalable way to build intelligent systems that combine the strengths of multiple specialized agents. With built-in orchestration patterns, a unified development model, and runtime features for managing execution, you can quickly prototype, refine, and deploy collaborative AI workflows. The framework provides the tools to turn multiple agents into a cohesive problem-solving team, whether you're running parallel processes, sequential workflows, or dynamic conversations.