Multi-Agent Architecture Patterns in Production

Problem Context

Single-agent systems hit a ceiling fast. Ask one LLM to research, plan, write code, review it, and deploy — and you get mediocre results at every step. The model tries to do everything and does nothing well.

Multi-agent architectures split complex tasks across specialized agents, each with focused system prompts, tools, and responsibilities. But "just add more agents" is not a strategy. The coordination overhead, failure cascading, and debugging complexity grow faster than the benefits — unless you pick the right pattern for your problem.

Concept Explanation

There are three dominant multi-agent patterns in production systems. Each makes fundamentally different trade-offs around control, flexibility, and reliability.

Pattern 1: Orchestrator

A central agent decomposes the task, delegates subtasks to worker agents, and synthesizes their outputs. The orchestrator sees the full picture; workers see only their slice.

      flowchart TD
          U["User Request"] --> O["Orchestrator Agent"]
          O -->|"Research X"| A1["Research Agent"]
          O -->|"Write code for Y"| A2["Coding Agent"]
          O -->|"Review output"| A3["Review Agent"]
          A1 -->|"findings"| O
          A2 -->|"code"| O
          A3 -->|"feedback"| O
          O --> R["Final Response"]
      
          style O fill:#4f46e5,color:#fff,stroke:#4338ca
          style A1 fill:#059669,color:#fff,stroke:#047857
          style A2 fill:#7c3aed,color:#fff,stroke:#6d28d9
          style A3 fill:#d97706,color:#fff,stroke:#b45309
      

When to use: Well-defined workflows where you know the steps upfront. Code generation pipelines, document analysis, multi-step data processing.

Trade-off: The orchestrator is a single point of intelligence — if it misunderstands the task, every downstream agent does wasted work. Strong orchestrator prompts are critical.

Pattern 2: Supervisor

Similar to the orchestrator, but the supervisor monitors agent outputs and can redirect, retry, or override. It adds a feedback loop: if the coding agent produces buggy code, the supervisor sends it back with the review agent's feedback.

      flowchart TD
          U["User Request"] --> S["Supervisor Agent"]
          S --> A1["Agent A"]
          S --> A2["Agent B"]
          A1 -->|"output"| S
          A2 -->|"output"| S
          S -->|"Not good enough"| A1
          S -->|"Revise with feedback"| A2
          S --> R["Approved Result"]
      
          style S fill:#dc2626,color:#fff,stroke:#b91c1c
          style A1 fill:#059669,color:#fff,stroke:#047857
          style A2 fill:#7c3aed,color:#fff,stroke:#6d28d9
      

When to use: Quality-critical workflows where outputs need validation. Content generation, code review, compliance checking.

Trade-off: Iteration loops can run away. Always set a max-iteration limit (typically 3). Each loop costs tokens and latency. A supervisor that's too strict will loop forever; too lenient defeats the purpose.

Pattern 3: Swarm (Peer-to-Peer)

No central coordinator. Agents hand off to each other based on the conversation state. Agent A decides "this needs coding expertise" and transfers control to Agent B, which might later hand off to Agent C for deployment.

When to use: Dynamic, open-ended tasks where the workflow can't be predetermined. Customer service (escalation paths), interactive assistants, exploratory research.

Trade-off: Hardest to debug. When something goes wrong, you're tracing a chain of handoffs between autonomous agents. Requires robust logging and the ability to replay agent decisions. OpenAI's Swarm framework implements this for experimentation, but production use demands careful guardrails.

Implementation

Orchestrator Pattern with Semantic Kernel

// Define specialized agents
      var researcher = new ChatCompletionAgent
      {
          Name = "Researcher",
          Instructions = """
              You research technical topics. Return structured findings with sources.
              Do NOT write code. Do NOT make recommendations.
              Focus only on gathering factual information.
              """,
          Kernel = kernel
      };
      
      var coder = new ChatCompletionAgent
      {
          Name = "Coder",
          Instructions = """
              You write production C# code based on provided requirements and research.
              Follow SOLID principles. Include error handling.
              Do NOT research — use only the information given to you.
              """,
          Kernel = kernel
      };
      
      var reviewer = new ChatCompletionAgent
      {
          Name = "Reviewer",
          Instructions = """
              Review code for bugs, security issues, and best practices.
              Be specific: cite line numbers and provide fixes.
              Rate overall quality: APPROVED or NEEDS_REVISION with reasons.
              """,
          Kernel = kernel
      };
      

// Orchestrator using AgentGroupChat with round-robin
      var chat = new AgentGroupChat(researcher, coder, reviewer)
      {
          ExecutionSettings = new()
          {
              TerminationStrategy = new ApprovalTerminationStrategy
              {
                  Agents = [reviewer],
                  MaximumIterations = 6,  // Safety limit
                  AutomaticReset = true
              },
              SelectionStrategy = new SequentialSelectionStrategy()
          }
      };
      
      await foreach (var message in chat.InvokeAsync())
      {
          Console.WriteLine($"[{message.AuthorName}]: {message.Content}");
      }
      

Supervisor Pattern with Evaluation Loop

public async Task<string> SupervisedGeneration(string task, int maxRetries = 3)
      {
          var worker = CreateWorkerAgent();
          var evaluator = CreateEvaluatorAgent();
      
          string output = null;
          string feedback = null;
      
          for (int i = 0; i < maxRetries; i++)
          {
              var prompt = feedback == null
                  ? task
                  : $"Revise based on feedback:\n{feedback}\n\nOriginal task: {task}";
      
              output = await worker.InvokeAsync(prompt);
      
              var evaluation = await evaluator.InvokeAsync(
                  $"Evaluate this output for: {task}\n\nOutput:\n{output}");
      
              if (evaluation.Contains("APPROVED"))
                  return output;
      
              feedback = evaluation;
              _logger.LogWarning("Supervisor iteration {i}: revision needed", i + 1);
          }
      
          _logger.LogError("Max retries reached, returning best effort");
          return output;
      }
      

Message Format Between Agents

Standardize how agents communicate. A structured handoff format prevents information loss:

{
          "from": "Researcher",
          "to": "Coder",
          "task_id": "abc-123",
          "context": {
              "original_request": "Build a rate limiter for our API",
              "findings": [
                  "Token bucket algorithm is standard for API rate limiting",
                  "Azure APIM supports built-in rate limiting policies",
                  "Redis is commonly used for distributed rate limiting state"
              ],
              "constraints": ["Must work in distributed environment", ".NET 8"]
          },
          "iteration": 1
      }
      

Pitfalls

Practical Takeaways