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Multi-Agent Coordination Patterns: Orchestrator-Workers & Evaluator-Optimizer
Autonomous Agents & Multi-Agent Swarms • Distributed Systems9 min read

Multi-Agent Coordination Patterns: Orchestrator-Workers & Evaluator-Optimizer

Structuring multi-agent architectures for complex software engineering and data pipelines.

Practice in Prompt LabTest in AI Interview

Core Conceptual Brief

Single agents break down on complex tasks due to context pollution. Master Orchestrator-Worker and Evaluator-Optimizer topologies to scale agent intelligence.

Theoretical & Empirical Architecture

As tasks become more complex, asking a single agent to handle research, code generation, testing, and deployment fills its context window with conflicting instructions and irrelevant noise.

Multi-Agent Coordination breaks a monolithic problem into specialized sub-agents with narrow scopes, distinct system prompts, and isolated context boundaries.

Under the Hood: Key Engineering Mechanics

1The Orchestrator-Workers Pattern

A high-level Orchestrator analyzes the user objective, breaks it into independent sub-tasks, dispatches them concurrently to specialized Worker agents (e.g. Researcher, Database Specialist, Frontend Builder), and synthesizes their individual deliverables into a final unified result.

2The Evaluator-Optimizer Pattern (Actor-Critic)

One agent (the Generator) produces candidate code or documentation. A second agent (the Evaluator) with a critical system prompt audits the work against strict test cases and security criteria. The Evaluator provides concrete feedback back to the Generator in an iterative refinement loop until a 100% pass score is achieved.

3Router-Specialists Pattern

A fast, inexpensive classifier model (e.g. Haiku or Flash) inspects incoming requests and routes them to specialized agents with tailored tools, preventing token waste on large models.

Evaluator-Optimizer Iterative Improvement Loop

async def evaluator_optimizer_loop(objective: str, max_iterations: int = 3):
    current_code = await code_generator_agent.draft(objective)
    
    for i in range(max_iterations):
        # Evaluator critiques the work
        evaluation = await evaluator_agent.critique(
            objective=objective, 
            candidate_code=current_code
        )
        
        print(f"Iteration {i + 1} Score: {evaluation.score}/100")
        
        if evaluation.passed:  # e.g. Score >= 95
            return current_code
            
        # Feed critique back to generator for targeted refinement
        current_code = await code_generator_agent.refine(
            current_code=current_code,
            feedback=evaluation.critique_points
        )
        
    return current_code

Production Guardrails & Takeaways

  • Use Orchestrator-Workers for broad, parallelizable tasks.
  • Use Evaluator-Optimizer loops when high quality and verifiable correctness are required.
  • Keep subagent context windows lean by passing only required task specifications.

Real Staff AI Systems Interview Question

Q: Why is an Evaluator-Optimizer multi-agent loop often superior to asking a single model to "write the code and make sure it has no bugs"?

Staff Answer:

A single model suffers from confirmation bias: it assumes the code it just generated is correct. Splitting the roles gives the Evaluator a dedicated persona specifically rewarded for finding bugs, edge-case failures, and security risks, resulting in significantly higher defect discovery rates.

Multi-Agent Coordination Patterns: Orchestrator-Workers & Evaluator-Optimizer | AI Systems Engineering Guide · DataArena