Principle:Microsoft Semantic kernel Multi Agent Orchestration

Overview

Multi-Agent Orchestration is the principle of coordinating multiple agents to work together through parallel, round-robin, or handoff patterns. In Microsoft Semantic Kernel, orchestrations define how agents collaborate -- whether they work simultaneously on the same problem, take turns in a structured group conversation, or dynamically hand off control to the most appropriate specialist.

This principle belongs to Workflow 3: Agent Conversation and Orchestration and represents the most advanced layer of the agent framework, enabling complex workflows that no single agent could handle alone.

Description

Why Multiple Agents?

While a single agent can be powerful, real-world tasks often benefit from decomposition into specialized roles:

• Division of expertise -- A writer agent produces content; a reviewer agent evaluates quality. Each has focused instructions optimized for its role.
• Parallel processing -- Multiple agents can process the same input simultaneously, providing diverse perspectives or handling independent subtasks.
• Dynamic routing -- A triage agent can assess a request and hand it off to the appropriate specialist (billing, technical support, refunds), creating adaptive workflows.

Multi-agent orchestration formalizes these collaboration patterns into reusable, configurable structures.

Orchestration Patterns

Semantic Kernel provides three built-in orchestration patterns:

Concurrent Orchestration

All agents receive the same input simultaneously and work in parallel. Each agent independently processes the message, and all responses are collected as an array.

Use cases:

• Getting multiple perspectives on a topic (e.g., writer and reviewer both evaluate a prompt).
• Running parallel analysis (e.g., sentiment analysis and key phrase extraction simultaneously).
• A/B testing different agent configurations.

Characteristics:

• Agents do not see each other's responses.
• All agents receive the identical input message.
• Results are returned as string[] (one result per agent).

Group Chat Orchestration

Agents take turns in a managed conversation, each seeing the messages produced by previous agents. A GroupChatManager controls the conversation flow, determining turn order and termination conditions.

Use cases:

• Iterative refinement (writer produces draft, reviewer critiques, writer revises).
• Collaborative problem-solving where agents build on each other's contributions.
• Debate or deliberation scenarios where multiple viewpoints converge.

Characteristics:

• Agents share a common conversation visible to all participants.
• A manager (e.g., RoundRobinGroupChatManager) controls whose turn it is and when to stop.
• The MaximumInvocationCount limits the total number of turns.

Handoff Orchestration

Agents dynamically transfer control to other agents based on the conversation context. Each agent has a defined set of agents it can hand off to, creating a directed graph of possible transitions.

Use cases:

• Customer service routing (triage -> billing, triage -> technical support, triage -> refunds).
• Multi-step workflows where different phases require different expertise.
• Escalation patterns where a general agent hands off to a specialist.

Characteristics:

• One agent is designated as the starting agent.
• Handoff rules define which agents can transfer to which other agents.
• The agent decides when to hand off based on conversation context and its instructions.
• Only one agent is active at any given time.

Orchestration Architecture

All orchestration patterns share a common architecture:

1. Agent Collection -- The set of agents that participate in the orchestration.
2. Orchestration Logic -- The pattern-specific rules for how agents interact (parallel, turn-based, or handoff).
3. Runtime -- An InProcessRuntime that manages message passing between agents within the orchestration.
4. Result Collection -- An OrchestrationResult<T> that provides the final output after all agents have completed.

The orchestration itself does not invoke agents directly. Instead, it configures how the runtime should route messages between agents, and the runtime handles the actual invocations.

Usage

Multi-Agent Orchestration is used whenever:

• A task requires multiple specialized perspectives or skills.
• Work can be parallelized across independent agents.
• A workflow requires dynamic routing based on input content.
• Iterative refinement between agents is needed.

Concurrent Orchestration

ChatCompletionAgent writer = new()
{
    Name = "Writer",
    Instructions = "You are a creative writer. Write a short story on the given topic.",
    Kernel = kernel
};

ChatCompletionAgent reviewer = new()
{
    Name = "Reviewer",
    Instructions = "You are a literary critic. Review and critique the given topic.",
    Kernel = kernel
};

ConcurrentOrchestration orchestration = new(writer, reviewer);

Group Chat Orchestration

GroupChatOrchestration groupChat = new(
    new RoundRobinGroupChatManager { MaximumInvocationCount = 5 },
    writer, reviewer);

Handoff Orchestration

HandoffOrchestration handoff = new(
    OrchestrationHandoffs.StartWith(triageAgent)
        .Add(triageAgent, [billingAgent, refundAgent])
        .Add(billingAgent, [triageAgent]),
    triageAgent, billingAgent, refundAgent);

Theoretical Basis

Multi-Agent Orchestration draws from several foundational concepts:

Multi-Agent Systems (MAS) from distributed AI research, where autonomous agents interact within an environment to achieve individual or collective goals. The orchestration patterns in Semantic Kernel implement specific well-studied interaction protocols from this field.

Workflow patterns from business process management, particularly the concepts of:

• Parallel split (concurrent orchestration) -- Work is divided across parallel branches.
• Synchronization (result collection) -- Parallel branches are merged before proceeding.
• Exclusive choice (handoff) -- A decision point routes work to one of several branches.
• Sequence (group chat with round-robin) -- Activities execute in a defined order.

Ensemble methods from machine learning, where multiple models are combined to produce better results than any single model. Concurrent orchestration is analogous to bagging, where multiple agents independently process the same input.

Related Pages

• Orchestration Patterns (Implementation) -- The concrete APIs for creating orchestrations.
• Orchestration Runtime (Principle) -- The runtime that powers orchestrations.
• InProcessRuntime (Implementation) -- The runtime implementation used by orchestrations.
• Orchestration Result Collection (Principle) -- Collecting results from orchestrated agents.
• OrchestrationResult GetValueAsync (Implementation) -- The API for retrieving orchestration results.
• Chat Completion Agent Creation (Principle) -- Creating the agents used in orchestrations.

Implemented By

• Implementation:Microsoft_Semantic_kernel_Orchestration_Patterns