Table of Contents

🎭 Understanding AI Agent Orchestration in LM-Kit.NET


📄 TL;DR

AI Agent Orchestration is the coordination of multiple AI agents to collaboratively solve complex tasks. Rather than relying on a single agent, orchestration patterns enable specialized agents to work together (sequentially, in parallel, or through dynamic routing), combining their unique capabilities to achieve outcomes no single agent could accomplish alone. In LM-Kit.NET, the LMKit.Agents.Orchestration namespace provides four orchestration patterns: Pipeline, Parallel, Router, and Supervisor, enabling sophisticated multi-agent workflows running entirely on-device.


📚 What is Agent Orchestration?

Definition: Agent Orchestration is the discipline of coordinating multiple AI agents to work together on complex tasks. It involves:

  • Task Decomposition: Breaking complex goals into sub-tasks
  • Agent Selection: Choosing the right agent for each sub-task
  • Execution Coordination: Managing the flow of work between agents
  • Result Aggregation: Combining outputs into coherent final results

Why Orchestration Matters

Single agents have limitations:

  • Context constraints: Limited context windows
  • Expertise boundaries: One agent may not have all required skills
  • Performance bottlenecks: Sequential processing of complex tasks
  • Error propagation: A single failure affects the entire workflow

Orchestration addresses these by enabling:

  • Specialization: Each agent focuses on what it does best
  • Parallelization: Independent tasks run concurrently
  • Fault tolerance: Failed agents can be retried or bypassed
  • Scalability: Add agents as complexity grows

🎯 The Four Orchestration Patterns

Pattern Overview

+---------------------------------------------------------------------------+
|                    LM-Kit.NET Orchestration Patterns                      |
+---------------------------------------------------------------------------+
|                                                                           |
|  +-----------------+  +-----------------+  +-----------------+           |
|  |    Pipeline     |  |    Parallel     |  |     Router      |           |
|  |                 |  |                 |  |                 |           |
|  |   A ----> B ----> C |  |   +---> B -+      |  |       +---> B     |           |
|  |                 |  | A-┼---> C -┼--->D   |  |   A --┼---> C     |           |
|  |  Sequential     |  |   +---> D -+      |  |       +---> D     |           |
|  |  Processing     |  |  Concurrent     |  |  Dynamic        |           |
|  |                 |  |  Execution      |  |  Selection      |           |
|  +-----------------+  +-----------------+  +-----------------+           |
|                                                                           |
|  +---------------------------------------------------------------------+ |
|  |                          Supervisor                                  | |
|  |                                                                      | |
|  |              +---------------------------+                          | |
|  |              |     Supervisor Agent      |                          | |
|  |              |   (Plans & Delegates)     |                          | |
|  |              +---------+-----------------+                          | |
|  |                        |                                            | |
|  |              +---------┼---------+                                  | |
|  |              v         v         v                                  | |
|  |          Agent A   Agent B   Agent C                                | |
|  |                                                                      | |
|  +---------------------------------------------------------------------+ |
|                                                                           |
+---------------------------------------------------------------------------+

🔄 Pipeline Orchestration

When to use: Tasks that require sequential processing where each step depends on the previous step's output.

How It Works

Input ----> Agent A ----> Agent B ----> Agent C ----> Output
              |           |           |
              v           v           v
          Research     Draft      Polish

Each agent receives the output of the previous agent, transforming it for the next stage.

Example: Content Creation Pipeline

using LMKit.Model;
using LMKit.Agents;
using LMKit.Agents.Orchestration;

var model = LM.LoadFromModelID("gemma3:12b");

// Create specialized agents
var researchAgent = Agent.CreateBuilder(model)
    .WithSystemPrompt("You are a research specialist. Gather key facts and insights on topics.")
    .WithName("Researcher")
    .Build();

var writerAgent = Agent.CreateBuilder(model)
    .WithSystemPrompt("You are a content writer. Create engaging articles from research notes.")
    .WithName("Writer")
    .Build();

var editorAgent = Agent.CreateBuilder(model)
    .WithSystemPrompt("You are an editor. Polish content for clarity, grammar, and style.")
    .WithName("Editor")
    .Build();

// Create pipeline orchestrator
var pipeline = new PipelineOrchestrator();
pipeline.AddAgent(researchAgent);
pipeline.AddAgent(writerAgent);
pipeline.AddAgent(editorAgent);

// Execute pipeline
var result = await pipeline.ExecuteAsync(
    "Write a blog post about sustainable energy trends in 2024",
    CancellationToken.None
);

Console.WriteLine(result.FinalOutput);

Best Use Cases

  • Content creation workflows (research → write → edit → publish)
  • Data processing chains (extract → transform → validate → load)
  • Multi-stage analysis (collect → analyze → summarize → recommend)

⚡ Parallel Orchestration

When to use: Tasks with independent sub-components that can be processed simultaneously.

How It Works

              +----> Agent A ---+
              |               |
Input --------┼----> Agent B ---┼------> Aggregated Output
              |               |
              +----> Agent C ---+

All agents work on the same input concurrently, and results are combined.

Example: Multi-Perspective Document Review

using LMKit.Model;
using LMKit.Agents;
using LMKit.Agents.Orchestration;

var model = LM.LoadFromModelID("gemma3:12b");

// Create perspective-specific agents
var legalReviewer = Agent.CreateBuilder(model)
    .WithSystemPrompt("You are a legal expert. Review documents for legal risks and compliance issues.")
    .WithName("Legal Reviewer")
    .Build();

var technicalReviewer = Agent.CreateBuilder(model)
    .WithSystemPrompt("You are a technical expert. Review documents for technical accuracy and feasibility.")
    .WithName("Technical Reviewer")
    .Build();

var businessReviewer = Agent.CreateBuilder(model)
    .WithSystemPrompt("You are a business analyst. Review documents for business impact and ROI.")
    .WithName("Business Reviewer")
    .Build();

// Create parallel orchestrator
var parallel = new ParallelOrchestrator();
parallel.AddAgent(legalReviewer);
parallel.AddAgent(technicalReviewer);
parallel.AddAgent(businessReviewer);

// Execute in parallel
var result = await parallel.ExecuteAsync(
    contractDocument,
    CancellationToken.None
);

// Access individual agent results
foreach (var agentResult in result.AgentResults)
{
    Console.WriteLine($"=== {agentResult.AgentName} ===");
    Console.WriteLine(agentResult.Output);
}

Best Use Cases

  • Multi-perspective analysis (legal, technical, business reviews)
  • Ensemble approaches (multiple agents answer, best is selected)
  • Redundancy for reliability (same task, multiple agents for verification)
  • Independent sub-tasks within a larger workflow

🔀 Router Orchestration

When to use: Tasks that need to be dynamically routed to the most appropriate specialist agent.

How It Works

              +----> Billing Agent (if billing query)
              |
Input ----> Router ----> Support Agent (if support issue)
              |
              +----> Sales Agent (if sales inquiry)

A routing mechanism (semantic matching or classification) directs requests to specialized handlers.

Example: Smart Customer Service Router

using LMKit.Model;
using LMKit.Agents;
using LMKit.Agents.Orchestration;

var model = LM.LoadFromModelID("gemma3:12b");

// Create specialized customer service agents
var billingAgent = Agent.CreateBuilder(model)
    .WithSystemPrompt("You are a billing specialist. Help customers with invoices, payments, and account charges.")
    .WithName("Billing Specialist")
    .Build();

var techSupportAgent = Agent.CreateBuilder(model)
    .WithSystemPrompt("You are a technical support expert. Help customers troubleshoot product issues.")
    .WithName("Tech Support")
    .Build();

var salesAgent = Agent.CreateBuilder(model)
    .WithSystemPrompt("You are a sales representative. Help customers with product information and purchases.")
    .WithName("Sales Rep")
    .Build();

// Create router orchestrator with semantic routing
var router = new RouterOrchestrator(model);
router.AddAgent(billingAgent, "Billing questions, invoice issues, payment problems, charges, refunds");
router.AddAgent(techSupportAgent, "Technical problems, bugs, errors, not working, troubleshooting");
router.AddAgent(salesAgent, "Product inquiries, pricing, features, purchasing, upgrades");

// Route and execute
var result = await router.ExecuteAsync(
    "I'm seeing an error code E-403 when I try to log in",
    CancellationToken.None
);

Console.WriteLine($"Routed to: {result.SelectedAgent}");
Console.WriteLine(result.Output);

Best Use Cases

  • Customer service triage (route to appropriate department)
  • Expert selection (choose specialist based on question domain)
  • Load balancing (distribute work across capable agents)
  • Intent-based workflows (action depends on detected intent)

👔 Supervisor Orchestration

When to use: Complex tasks requiring intelligent decomposition, delegation, and coordination.

How It Works

                    +-------------------------+
                    |    Supervisor Agent     |
                    |                         |
                    | • Analyzes task         |
                    | • Creates plan          |
                    | • Delegates to workers  |
                    | • Aggregates results    |
                    +-----------+-------------+
                                |
              +-----------------┼-----------------+
              v                 v                 v
        +-----------+    +-----------+    +-----------+
        |  Agent A  |    |  Agent B  |    |  Agent C  |
        | (Worker)  |    | (Worker)  |    | (Worker)  |
        +-----------+    +-----------+    +-----------+

A supervisor agent plans and coordinates, delegating work to specialized worker agents.

Example: Research Team Supervisor

using LMKit.Model;
using LMKit.Agents;
using LMKit.Agents.Orchestration;
using LMKit.Agents.Tools.BuiltIn;

var model = LM.LoadFromModelID("glm4.7-flash");

// Create worker agents with specific tools
var webResearcher = Agent.CreateBuilder(model)
    .WithSystemPrompt("You research topics on the web and gather current information.")
    .WithName("Web Researcher")
    .WithTools(tools => tools.Register(BuiltInTools.WebSearch))
    .Build();

var dataAnalyst = Agent.CreateBuilder(model)
    .WithSystemPrompt("You analyze data and create statistical summaries.")
    .WithName("Data Analyst")
    .WithTools(tools => tools.Register(BuiltInTools.Calculator))
    .Build();

var reportWriter = Agent.CreateBuilder(model)
    .WithSystemPrompt("You write clear, professional reports from research findings.")
    .WithName("Report Writer")
    .Build();

// Create supervisor orchestrator
var supervisor = new SupervisorOrchestrator(model);
supervisor.AddWorker(webResearcher, "Web research, current events, online information gathering");
supervisor.AddWorker(dataAnalyst, "Data analysis, statistics, calculations, numerical insights");
supervisor.AddWorker(reportWriter, "Report writing, summarization, professional documentation");

// Execute with supervisor coordination
var result = await supervisor.ExecuteAsync(
    "Research the current state of AI adoption in healthcare, analyze key statistics, and produce an executive summary.",
    CancellationToken.None
);

Console.WriteLine(result.FinalOutput);

Best Use Cases

  • Complex research projects (coordinate multiple research streams)
  • Multi-stage workflows with interdependencies
  • Adaptive task execution (supervisor adjusts plan based on intermediate results)
  • Quality-controlled pipelines (supervisor reviews and requests revisions)

🎯 Choosing the Right Pattern

Pattern Use When Strengths Considerations
Pipeline Sequential dependencies exist Clear flow, easy debugging Bottlenecks at slow stages
Parallel Tasks are independent Fast execution, multiple perspectives Aggregation complexity
Router Need dynamic specialist selection Efficient resource use Routing accuracy critical
Supervisor Complex, adaptive workflows Flexible, intelligent coordination Higher overhead

Decision Flow

                    +------------------------+
                    | Is the task complex    |
                    | with interdependencies?|
                    +-----------+------------+
                                |
                    +-----------+-----------+
                    |                       |
                   Yes                      No
                    |                       |
                    v                       v
            +---------------+      +--------------------+
            |  Supervisor   |      | Are sub-tasks      |
            |               |      | independent?       |
            +---------------+      +---------+----------+
                                             |
                                   +---------+---------+
                                   |                   |
                                  Yes                  No
                                   |                   |
                                   v                   v
                           +---------------+   +---------------+
                           |   Parallel    |   | Need routing? |
                           |               |   +-------+-------+
                           +---------------+           |
                                             +---------+---------+
                                             |                   |
                                            Yes                  No
                                             |                   |
                                             v                   v
                                     +---------------+   +---------------+
                                     |    Router     |   |   Pipeline    |
                                     |               |   |               |
                                     +---------------+   +---------------+

📖 Key Terms

  • Orchestrator: The component responsible for coordinating agent execution
  • Worker Agent: An agent that performs specific tasks under orchestration
  • Task Decomposition: Breaking a complex task into manageable sub-tasks
  • Result Aggregation: Combining outputs from multiple agents into a unified result
  • Routing Logic: The mechanism for selecting which agent handles a request
  • Delegation: Assigning specific sub-tasks to appropriate agents
  • Supervisor Pattern: A hierarchical approach where one agent coordinates others

📚 Related API Documentation


🔗 Related Glossary Topics


🌐 External Resources


📝 Summary

AI Agent Orchestration enables multiple specialized AI agents to collaborate on complex tasks that exceed the capabilities of any single agent. In LM-Kit.NET, four orchestration patterns address different workflow needs: Pipeline for sequential processing, Parallel for concurrent execution, Router for dynamic specialist selection, and Supervisor for intelligent coordination with planning and delegation. By choosing the appropriate pattern (or combining them), developers can build sophisticated multi-agent systems that decompose complex problems, leverage specialized expertise, and aggregate results into coherent solutions, all running locally on-device for maximum privacy and control.