Claude Opus Orchestrator-Sonnet-Worker Architecture

The orchestrator-worker pattern ways to structure complex AI agent systems. By combining Claude Opus as the central orchestrator with specialized Sonnet workers handling discrete subtasks, developers can build systems that balance reasoning capability with cost efficiency and task specialization.

Understanding the Architecture

At its core, an orchestrator-sonnet-worker architecture consists of three layers working in concert. The orchestrator (typically Claude Opus) handles high-level planning, context management, and coordinating multiple specialized workers. Each worker runs a Claude Sonnet instance focused on a specific domain—code review, documentation generation, test writing, or file operations. The architecture creates a hierarchy where Opus provides the “big picture” thinking while Sonnet workers execute focused tasks.

This separation works because Opus excels at complex reasoning, and understanding ambiguous requirements. Sonnet, being faster and more cost-effective, handles the heavy lifting of repetitive or narrowly scoped tasks. The orchestrator maintains the overall state and delegates appropriately.

When to Use This Pattern

You should consider this architecture when building systems that require multiple distinct skill domains. A single AI model attempting to handle frontend design, backend logic, database schema, and API integration often produces inconsistent results. By assigning each domain to a specialized Sonnet worker, you get consistent, predictable outputs.

For example, if you need a system that can generate frontend code, write unit tests, create PDF documentation, and manage a memory layer, an orchestrator coordinating four specialized Sonnet workers will outperform a single model trying to do everything.

Building the Orchestrator

The orchestrator serves as the system’s brain. It receives the user’s request, decomposes it into subtasks, selects appropriate workers, aggregates results, and presents the final output. Here’s a conceptual implementation:

class ClaudeOrchestrator:
    def __init__(self, workers: dict):
        self.workers = workers  # {"frontend": sonnet_worker, "tdd": tdd_worker}
    
    def process(self, user_request: str):
        # Opus-level reasoning: decompose the request
        subtasks = self.decompose(user_request)
        
        results = []
        for task in subtasks:
            worker = self.select_worker(task)
            result = worker.execute(task)
            results.append(result)
        
        # Opus synthesizes the final response
        return self.synthesize(results)

The orchestrator uses Opus to understand the request’s intent and map it to worker capabilities. This is where Claude Code’s native tool use shines—the orchestrator can read files, execute bash commands, and manage the overall workflow without needing explicit programming for each edge case.

Defining Worker Specializations

Each Sonnet worker needs a clear domain and well-defined boundaries. The skill definition becomes the worker’s “persona” and capability boundary. Consider these common worker types:

Frontend Worker — Specializes in UI code, component design, and styling. If you’re using the frontend-design skill, this worker would handle React components, Tailwind configurations, and responsive layouts. It should receive context about design requirements and existing codebase patterns.

Test-Driven Development Worker — Focused on writing tests before code, following TDD principles. This worker reads the specification, writes failing tests, then outputs the implementation that makes those tests pass. The tdd skill provides excellent patterns for this worker type.

Documentation Worker — Handles API docs, README files, and technical writing. Using the pdf skill, this worker can generate formatted documentation packages. It understands code structure and produces clear, accurate documentation.

Memory and Context Worker — Manages persistent context across sessions. The supermemory skill enables this worker relevant information, maintaining continuity across complex multi-step projects.

Implementing Worker Communication

Workers communicate through structured messages. The orchestrator passes context to each worker and collects results. Here’s how this might work in practice:

def execute_with_worker(worker, task_context):
    message = {
        "task": task_context.description,
        "files": task_context.files_to_read,
        "constraints": task_context.constraints,
        "output_format": task_context.format
    }
    
    response = worker.chat(message)
    return {
        "worker": worker.name,
        "output": response,
        "success": response.status == "complete"
    }

The key principle: each worker should receive enough context to complete its task but not so much that it becomes confused by irrelevant details. This is where prompt engineering becomes critical—the orchestrator must filter and structure context for each worker appropriately.

Cost and Performance Considerations

Using Sonnet workers instead of Opus throughout offers significant cost advantages. Sonnet operates at roughly one-fifth the cost of Opus while maintaining strong performance on narrow tasks. A complex project that might cost $50 with pure Opus might cost $15-20 with an orchestrator-worker pattern.

Response times also improve. Sonnet workers typically respond faster than Opus for focused tasks. The orchestrator can parallelize independent subtasks, reducing overall latency. For a task broken into five independent subtasks, you could theoretically execute them concurrently rather than sequentially.

However, this architecture introduces overhead. The orchestrator needs to manage worker lifecycle, handle failures, and synthesize results. For simple, single-domain tasks, a direct Sonnet call will be more efficient. Reserve the orchestrator pattern genuinely complex projects requiring multiple skill domains.

Error Handling and Recovery

Well-designed systems must handle worker failures gracefully. If one worker fails, the orchestrator should retry with adjusted parameters or skip the failed subtask and continue. Here’s a pattern:

def execute_with_retry(worker, task, max_retries=2):
    for attempt in range(max_retries):
        try:
            return worker.execute(task)
        except WorkerError as e:
            if attempt == max_retries - 1:
                return {"error": str(e), "worker_failed": True}
            # Adjust parameters and retry
            task = adjust_task_parameters(task, e)

The orchestrator should maintain a task queue with dependencies tracked, allowing partial completion and intelligent resumption when issues occur.

Practical Application Example

Imagine building a feature that requires adding user authentication to an existing application. The orchestrator would:

1. Analyze the codebase to understand the current structure
2. Delegate database schema changes to a database worker
3. Send frontend authentication UI tasks to the frontend-design worker
4. Assign backend authentication logic to an API worker
5. Route test writing to the tdd worker
6. Request documentation updates from the docs worker

Each worker operates independently on its domain, returning results to the orchestrator. Opus then integrates these pieces, ensuring consistency and handling any conflicts between worker outputs.

Conclusion

The orchestrator-sonnet-worker pattern provides a powerful framework for building sophisticated AI agent systems. By combining Opus’s reasoning capabilities with specialized Sonnet workers, you get cost-effective, focused execution across multiple domains. The key is defining clear worker boundaries, implementing clear communication protocols, and designing graceful error handling.

Start by identifying distinct skill domains in your project, then assign each to a specialized worker. Let Opus handle the coordination and synthesis. This architecture scales well and produces more consistent results than asking a single model to handle everything.

Related Reading

• Claude Code Agent Pipeline: Sequential vs Parallel Execution — Compare sequential and parallel execution models for multi-agent workflows
• Multi-Agent Orchestration with Claude Subagents Guide — Build coordinated multi-agent systems with specialized subagents
• Claude Code Multi-Agent Subagent Communication Guide — Implement reliable communication protocols between orchestrator and worker agents
• Claude Skills Hub — Explore advanced orchestration patterns and multi-agent system design

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