AI Agent Orchestration Patterns: Designing Workflows

The patterns and practices for orchestrating complex AI agent workflows.

Orchestrating AI agents is like conducting an orchestra — knowing when each agent should play, how loud, and when to stop. This guide covers the design patterns for effective AI agent orchestration.

Pattern Catalog

1. Simple Sequential (Pipeline)

Task → Agent 1 → Agent 2 → Agent 3 → Result

Best for: Linear processes like content creation

workflow = SequentialPipeline([
    ("research", research_agent),
    ("outline", outline_agent),
    ("write", writer_agent),
    ("edit", editor_agent),
    ("publish", publisher_agent),
])

2. Parallel Scatter-Gather

     ┌→ Agent A ┐
Task ├→ Agent B ├→ Merge → Result
     └→ Agent C ┘

Best for: Getting multiple perspectives

3. Conditional Routing

Task → Classifier → ┌→ Path A (if billing)
                     ├→ Path B (if technical)
                     └→ Path C (if general)

Best for: Customer support, ticket routing

workflow = ConditionalRouter({
    "billing": billing_agent,
    "technical": tech_agent,
    "general": general_agent,
}, classifier=intent_classifier)

4. Loop Until Satisfied

Task → Agent → Quality Check → ┌→ Output (if quality > threshold)
                               └→ Agent (if quality < threshold)

Best for: Iterative improvement

async def loop_until_satisfied(task, max_iterations=3):
    result = await agent.process(task)
    for _ in range(max_iterations):
        quality = await quality_checker.evaluate(result)
        if quality.score > 0.85:
            return result
        result = await improver_agent.improve(result, quality.feedback)
    return result

5. Human-in-the-Loop

Task → Agent → Human Review → ┌→ Approve → Output
                              └→ Reject → Agent (revise)

Best for: High-stakes decisions

async def human_in_loop(task):
    result = await agent.process(task)
    review = await request_human_review(result)

    if review.approved:
        return result
    else:
        # Agent revises based on feedback
        return await agent.process(f"{task}\n\nFeedback: {review.feedback}")

6. Competitive Selection

Task → ┌→ Agent A → Result A ┐
       ├→ Agent B → Result B ├→ Best Selector → Output
       └→ Agent C → Result C ┘

Best for: When quality matters more than cost

async def competitive(task):
    results = await asyncio.gather(
        agent_a.process(task),
        agent_b.process(task),
        agent_c.process(task),
    )
    # Evaluate each result
    scored = [(await scorer.evaluate(r), r) for r in results]
    # Return best
    return max(scored, key=lambda x: x[0].score)[1]

7. Map-Reduce

Task → Split into chunks → ┌→ Agent processes chunk 1 ┐
                           ├→ Agent processes chunk 2 ├→ Combine
                           └→ Agent processes chunk N ┘

Best for: Processing large datasets

async def map_reduce(data, chunk_size=100):
    chunks = split_into_chunks(data, chunk_size)

    # Map: process each chunk
    results = await asyncio.gather(*[
        agent.process(chunk) for chunk in chunks
    ])

    # Reduce: combine results
    return await combiner_agent.process(results)

State Management

class WorkflowState:
    """Manages state across workflow steps."""
    def __init__(self):
        self.data = {}
        self.history = []

    def set(self, key, value):
        self.data[key] = value
        self.history.append({
            "key": key,
            "value": value,
            "timestamp": datetime.utcnow(),
        })

    def get(self, key, default=None):
        return self.data.get(key, default)

    def checkpoint(self):
        """Save state for recovery."""
        return copy.deepcopy(self.data)

    def restore(self, checkpoint):
        """Restore from checkpoint."""
        self.data = checkpoint

Error Handling Patterns

Retry with Backoff

async def with_retry(agent, task, max_retries=3):
    for attempt in range(max_retries):
        try:
            return await agent.process(task)
        except (TimeoutError, RateLimitError) as e:
            if attempt == max_retries - 1:
                raise
            wait = 2 ** attempt  # Exponential backoff
            await asyncio.sleep(wait)

Fallback Chain

async def with_fallback(task, agent_chain):
    """Try agents in order until one succeeds."""
    for agent in agent_chain:
        try:
            return await agent.process(task)
        except AgentError:
            continue
    raise AllAgentsFailedError()

Circuit Breaker

class CircuitBreaker:
    def __init__(self, threshold=5, reset_timeout=60):
        self.failures = 0
        self.threshold = threshold
        self.reset_timeout = reset_timeout
        self.last_failure = None

    async def call(self, func, *args):
        if self.is_open():
            raise CircuitOpenError("Circuit breaker is open")

        try:
            result = await func(*args)
            self.failures = 0  # Reset on success
            return result
        except Exception:
            self.failures += 1
            self.last_failure = datetime.utcnow()
            raise

Cost-Aware Orchestration

class CostAwareOrchestrator:
    def __init__(self, budget):
        self.budget = budget
        self.spent = 0

    async def execute(self, workflow):
        for step in workflow.steps:
            # Check budget
            estimated_cost = self.estimate_cost(step)
            if self.spent + estimated_cost > self.budget:
                # Use cheaper alternative
                step = self.downgrade_step(step)

            result = await step.agent.process(step.input)
            self.spent += result.cost

        return result

    def downgrade_step(self, step):
        """Use a cheaper model/agent for this step."""
        if step.agent.model == "gpt-4o":
            step.agent = Agent(model="gpt-4o-mini")
        return step

Conclusion

Choosing the right orchestration pattern dramatically affects the quality, speed, and cost of your AI agent workflows. Start simple (sequential), add complexity only when needed (parallel, conditional, loops), and always plan for failures and budget constraints.

Learn More

• Multi-Agent Systems Design
• AI Agent Orchestration Tools
• AI Workflow Automation Examples
• AI Agent Cost Optimization

Find orchestration tools on SkillExchange.