Agent Orchestration Patterns: How to Coordinate Multiple AI Agents in 2026

Building one AI agent is hard. Building a system where multiple agents collaborate? That's where most teams hit a wall.

As autonomous AI agents move from demos to production, the question shifts from "can we build an agent?" to "how do we make agents work together?" This guide covers the five orchestration patterns that actually work in production — and when to use each.

Why Orchestration Matters

A single agent can handle simple workflows: answer questions, summarize documents, send notifications. But real business processes involve handoffs, dependencies, and parallel work.

Think about what happens when a customer submits a support ticket:

• One agent triages and classifies the issue
• Another searches the knowledge base for solutions
• A third checks the customer's account history
• A fourth drafts a response
• A human (or supervisor agent) approves and sends

Without orchestration, you're duct-taping API calls together. With it, you have a system.

Pattern 1: Sequential Pipeline

How it works: Agents execute in order. Output from Agent A becomes input for Agent B.

Input → Agent A (extract) → Agent B (analyze) → Agent C (format) → Output

Best for: Document processing, content creation, data transformation.

Pros: Simple, predictable, easy to debug.

Cons: Slow (no parallelism), single point of failure at each stage.

When to use it: When each step genuinely depends on the previous one. Don't force a pipeline when steps could run in parallel.

Example: Web Research Pipeline

import requests

# Agent A: Scrape multiple sources
def scrape_agent(urls):
    results = []
    for url in urls:
        resp = requests.get(
            "https://api.mantisapi.com/v1/scrape",
            params={"url": url},
            headers={"Authorization": "Bearer YOUR_API_KEY"}
        )
        results.append(resp.json())
    return results

# Agent B: Extract structured data
def extract_agent(raw_pages):
    extracted = []
    for page in raw_pages:
        resp = requests.post(
            "https://api.mantisapi.com/v1/extract",
            json={"url": page["url"], "schema": {"title": "string", "summary": "string"}},
            headers={"Authorization": "Bearer YOUR_API_KEY"}
        )
        extracted.append(resp.json())
    return extracted

# Agent C: Synthesize report (using an LLM)
def report_agent(data):
    # Feed extracted data to GPT-4o for synthesis
    return synthesize_with_llm(data)

Pattern 2: Fan-Out / Fan-In

How it works: A coordinator agent sends the same (or different) tasks to multiple agents simultaneously, then collects and merges results.

           → Agent A (source 1) →
Coordinator → Agent B (source 2) → Merger → Output
           → Agent C (source 3) →

Best for: Research tasks, competitive analysis, multi-source data gathering.

Pros: Fast (parallel execution), resilient (one failure doesn't block all).

Cons: Merging results is hard, inconsistent outputs across agents.

Production tip: Set timeouts per agent. If one agent hangs, don't let it block the entire workflow. Return partial results with a confidence score.

Example: Competitive Price Monitoring

import asyncio
import aiohttp

async def scrape_competitor(session, competitor_url, api_key):
    """Each 'agent' scrapes one competitor site."""
    async with session.post(
        "https://api.mantisapi.com/v1/extract",
        json={
            "url": competitor_url,
            "schema": {
                "product_name": "string",
                "price": "number",
                "currency": "string",
                "in_stock": "boolean"
            }
        },
        headers={"Authorization": f"Bearer {api_key}"}
    ) as resp:
        return await resp.json()

async def fan_out_price_check(competitors, api_key):
    """Fan-out: scrape all competitors in parallel."""
    async with aiohttp.ClientSession() as session:
        tasks = [scrape_competitor(session, url, api_key) for url in competitors]
        results = await asyncio.gather(*tasks, return_exceptions=True)
    
    # Fan-in: merge results, filter failures
    prices = []
    for r in results:
        if not isinstance(r, Exception):
            prices.append(r)
    return prices

Pattern 3: Supervisor / Worker

How it works: A supervisor agent decomposes a complex task, delegates subtasks to worker agents, reviews their output, and iterates if needed.

Supervisor
├── assigns task → Worker A → reviews output ✓
├── assigns task → Worker B → reviews output ✗ → reassigns
└── assigns task → Worker C → reviews output ✓
→ Supervisor synthesizes final answer

Best for: Complex problem-solving, code generation, multi-step reasoning.

Pros: Quality control built in, handles ambiguity well.

Cons: Supervisor becomes bottleneck, higher token cost (review loops).

Key insight: The supervisor doesn't need to be the smartest model — it needs to be the best judge. Use a reasoning model for supervision and faster models for workers.

Pattern 4: Event-Driven / Reactive

How it works: Agents listen for events and react independently. No central coordinator — agents are triggered by conditions in the environment.

Event Bus
├── "new_ticket"       → Triage Agent
├── "payment_failed"   → Recovery Agent
├── "user_inactive_7d" → Re-engagement Agent
└── "deploy_complete"  → QA Agent

Best for: Monitoring, alerting, real-time systems, customer support.

Pros: Highly scalable, loosely coupled, easy to add new agents.

Cons: Hard to reason about system behavior, potential race conditions.

Production tip: Always include a dead-letter queue. When an agent fails to process an event, you need visibility — silent failures in event-driven systems are brutal to debug.

Pattern 5: Consensus / Debate

How it works: Multiple agents independently tackle the same problem, then compare answers. A judge agent (or voting mechanism) selects the best output.

Problem → Agent A → Answer A ─┐
Problem → Agent B → Answer B ──┼→ Judge → Final Answer
Problem → Agent C → Answer C ─┘

Best for: High-stakes decisions, code review, content quality, safety-critical systems.

Pros: Higher accuracy, catches individual agent errors.

Cons: 3x+ the cost, slower, judge can be wrong too.

When it's worth it: When the cost of a wrong answer exceeds the cost of running 3 agents. Medical triage? Yes. Generating a tweet? Probably not.

Choosing the Right Pattern

Most production systems combine patterns. A supervisor might fan-out subtasks, with event-driven triggers kicking off the whole workflow.

Implementation Checklist

Before you ship a multi-agent system:

1. Define agent boundaries — Each agent should have a clear, single responsibility
2. Standardize message formats — Agents need a common language (JSON schemas work)
3. Add observability — Log every agent interaction. You will need to debug this.
4. Set timeouts everywhere — Agents can loop forever. Don't let them.
5. Plan for partial failure — What happens when 1 of 4 agents fails? Degrade gracefully.
6. Version your agents — When Agent B changes, does Agent A still work?
7. Start simple — Use a pipeline. Add complexity only when you have evidence you need it.

Giving Agents Real-World Data

Every orchestration pattern above assumes agents can access real-world data. That's where web scraping comes in. Whether your agents are researching competitors, monitoring prices, or gathering leads, they need reliable access to web data.

The WebPerception API gives your agents three core capabilities:

• Scrape — Get clean, rendered content from any URL (handles JavaScript, anti-bot, proxies)
• Extract — Pull structured data using AI (define a schema, get JSON back)
• Screenshot — Capture visual snapshots for monitoring and verification

These become the tools your worker agents call. The orchestration pattern handles coordination; the API handles the messy reality of the web.

What's Next

Multi-agent orchestration is moving fast. The teams winning today are the ones who picked a pattern, shipped it, and iterated — not the ones still designing the perfect architecture on a whiteboard.

Start with a two-agent pipeline. Get it working. Then evolve.