n8n Multi-Agent Orchestration: Coordinate AI Agent Teams That Actually Work

Your single AI agent is drowning. You just don’t see it yet.

You keep adding tools. Expanding the system prompt. Tweaking model temperature. But the more capable you try to make it, the more unreliable it becomes.

Responses get slower. Costs climb. The agent starts choosing wrong tools or getting stuck in loops.

This scenario plays out constantly in the n8n community. Developers build impressive single-agent workflows, hit a complexity ceiling, and wonder what went wrong.

The answer usually isn’t a smarter model or better prompts. It’s architecture.

The Single Agent Ceiling

Every AI agent has a breaking point.

The research is clear: agents with more than 5-7 tools start making worse decisions. Context windows fill up. Debugging becomes impossible because you can’t isolate which part of the monolithic agent failed.

The symptoms look familiar:

• Agent picks the wrong tool for obvious requests
• Response times stretch from seconds to minutes
• Token costs spiral without clear cause
• Testing becomes impossible because too many variables interact

What Multi-Agent Orchestration Actually Means

Multi-agent orchestration splits a complex AI system into specialized components that coordinate.

Instead of one overloaded agent trying to handle everything, you have a team:

• A supervisor that routes requests
• Specialists that handle specific domains
• Clear boundaries between responsibilities

Think of it like a well-run company. You don’t have one person handling sales, support, engineering, and accounting. You have departments with clear roles, managers who delegate, and communication channels between teams.

This mirrors what Microsoft’s AI architecture guidance calls “hierarchical coordination” in enterprise AI systems.

In n8n, this translates to multiple AI Agent nodes working together. A classifier decides where requests go. Specialist agents handle their domains with focused tool sets. Orchestrators coordinate multi-step tasks across agents.

What You’ll Learn

• Four orchestration patterns and when to use each
• Two implementation approaches in n8n: AI Agent Tool vs sub-workflows
• State management for maintaining context across agent handoffs
• Cost optimization strategies that can cut multi-agent costs by 40%+
• Three real-world examples with production configurations
• Error handling patterns that prevent cascading failures
• Common mistakes that sabotage multi-agent systems

When Single Agents Break Down

Before diving into multi-agent patterns, you need to recognize when a single agent has hit its limits. Not every workflow needs orchestration. Adding complexity without cause creates its own problems.

The Tool Threshold

Research and practical experience converge on a number: 5-7 tools per agent is the sweet spot. Beyond that, agent performance degrades in predictable ways.

Tool Count	Behavior
1-3 tools	Reliable tool selection, fast responses
4-6 tools	Generally good, occasional confusion
7-10 tools	Frequent wrong tool choices, slower reasoning
10+ tools	Unreliable, expensive, hard to debug

When an agent has 15 tools, every request requires the LLM to evaluate all 15 options. This consumes tokens, increases latency, and introduces decision fatigue for the model.

Context Window Saturation

Every tool needs a description. Every conversation turn gets stored in memory. Every system prompt instruction competes for space. A well-configured single agent might consume 2,000-4,000 tokens just in setup before any user input arrives.

Add a few conversation turns with memory, and you’re approaching context limits. The LLM starts forgetting earlier instructions. Responses become inconsistent.

The Debugging Nightmare

When a single agent misbehaves, where do you look? Was it the prompt? The tool description? The memory configuration? A specific tool’s output? Temperature settings?

With multiple potential failure points in one node, isolation becomes nearly impossible. You end up tweaking randomly and hoping something works.

Multi-agent architectures solve this by separating concerns. When the orders agent fails, you know exactly where to look. You can test it independently, fix it, and redeploy without touching the rest of the system.

Decision Framework

Use this quick test to decide if you need multi-agent orchestration:

Question	If Yes
Do you have more than 6 tools?	Consider splitting by domain
Does one agent handle unrelated tasks?	Route to specialists
Is debugging taking hours?	Separate concerns
Are costs unpredictable?	Isolate expensive operations
Do different tasks need different models?	Use agent-specific configurations

If you answered yes to none of these, a single agent is probably fine. For fundamentals on building effective single agents, see our AI Agent node guide.

Multi-Agent Architecture Patterns

Four patterns cover most multi-agent use cases. Each has trade-offs in complexity, latency, and cost. Choosing the right pattern depends on your specific requirements.

Supervisor/Delegator Pattern

A primary agent receives all requests and delegates to specialist agents. The supervisor decides which specialist handles each task based on the request content.

[User Input] → [Supervisor Agent] → [Specialist A]
                                  → [Specialist B]
                                  → [Specialist C]

How it works:

The supervisor agent has minimal tools of its own. Its job is understanding requests and routing them. Specialist agents have focused tool sets and domain-specific prompts. The supervisor calls specialists as tools using the LLM’s native function calling capabilities and aggregates their responses.

Best for:

• Customer support systems with distinct categories
• Research assistants with multiple data sources
• Any system with clear domain boundaries

Trade-offs:

• Adds one LLM call (supervisor) to every request
• Supervisor can become a bottleneck
• Specialists can’t easily collaborate on single requests

Routing Pattern

A classifier (often a simpler LLM call or even rule-based logic) routes requests before any agent processes them. Unlike the supervisor pattern, routing happens outside the agent system entirely.

[User Input] → [Classifier] → [Switch]
                                ├→ [Agent A]
                                ├→ [Agent B]
                                └→ [Agent C]

How it works:

Classification can be a simple LLM call asking “which category does this belong to?” or a Basic LLM Chain with structured output. The n8n Switch node routes to the appropriate agent based on the classification.

Best for:

• High-volume systems where routing cost matters
• Clear, non-overlapping request categories
• Situations where you want deterministic routing

Trade-offs:

• Misclassification sends requests to wrong agents
• Hard to handle requests spanning multiple categories
• Classification must be updated when categories change

For understanding when to use chains vs agents for classification, see our AI Agent vs LLM Chain comparison.

Pipeline Pattern

Agents process requests sequentially, each adding their contribution before passing to the next. Like an assembly line, each agent has one job.

[User Input] → [Agent A: Research] → [Agent B: Analysis] → [Agent C: Output] → [Result]

How it works:

Each agent receives the output of the previous agent as input. The first agent might gather information, the second analyzes it, the third formats the final response. Context flows forward through the pipeline.

Best for:

• Content creation workflows (research → write → edit)
• Data processing with distinct stages
• Any task with natural sequential phases

Trade-offs:

• Total latency is the sum of all agents
• Errors in early stages propagate forward
• No backtracking if later stages need more information

Parallel/Swarm Pattern

Multiple agents work simultaneously on different aspects of the same request. Results are aggregated at the end.

[User Input] → [Splitter] → [Agent A] ─┐
                          → [Agent B] ─┼→ [Aggregator] → [Result]
                          → [Agent C] ─┘

How it works:

A dispatcher sends the same request (or different aspects of it) to multiple agents concurrently. An aggregator node combines their responses. This is useful when you need multiple perspectives or when different agents can work independently.

Best for:

• Research requiring multiple data sources
• Validation requiring multiple checks
• Any task where independent analysis helps

Trade-offs:

• Aggregation can be complex
• Costs multiply (all agents run for every request)
• Not all tasks benefit from parallelism

Pattern Comparison

Pattern	Complexity	Latency	Cost	Best For
Supervisor	Medium	Medium (adds 1 call)	Medium	Clear domain delegation
Routing	Low	Low	Lowest	High-volume, distinct categories
Pipeline	Medium	High (sequential)	Medium	Sequential processing stages
Parallel	High	Low (concurrent)	Highest	Independent parallel tasks

Implementation Approaches in n8n

n8n offers two primary ways to build multi-agent systems. Each has strengths depending on your requirements.

The AI Agent Tool Node

n8n’s AI Agent Tool node enables multi-agent orchestration on a single canvas. You connect specialist agents as tools to a primary orchestrator agent.

How to set it up:

1. Create your primary orchestrator AI Agent node
2. Add AI Agent Tool nodes as sub-nodes connected to the primary agent
3. Each AI Agent Tool contains its own complete agent configuration
4. The orchestrator calls these as tools when needed

Configuration example for the tool description:

Handle all order-related requests. Use this for:
- Order status lookups
- Shipping information
- Order modifications
- Refund requests

Input: Customer query about their order
Output: Resolution or status update

When to use AI Agent Tool:

• Specialists are tightly coupled to the orchestrator
• You want everything visible on one canvas
• Context sharing between agents is straightforward
• You’re building a supervisor pattern

Advantages:

• Single workflow to maintain
• Visual overview of entire system
• Built-in context passing
• Simpler deployment

Sub-Workflow Orchestration

The Workflow Tool lets your agent call entirely separate n8n workflows. Each specialist lives in its own workflow with its own triggers, error handling, and deployment lifecycle.

How to set it up:

1. Create separate workflows for each specialist agent
2. Each specialist workflow has its own AI Agent node and tools
3. In your orchestrator workflow, add Workflow Tool nodes
4. Configure the Workflow Tool to call the appropriate specialist workflow

When to use sub-workflows:

• Specialists have complex internal logic
• Different teams own different agents
• You need independent deployment and testing
• Specialists might be reused across multiple orchestrators

Advantages:

• Independent testing and deployment
• Better organization for complex systems
• Specialists can have their own error handling
• Cleaner separation of concerns

Comparison Table

Factor	AI Agent Tool	Sub-Workflows
Visual complexity	Everything on one canvas	Spread across workflows
Deployment	Single workflow	Independent deployments
Testing	Test entire system	Test specialists in isolation
Team ownership	Harder to split	Natural boundaries
Error isolation	Shared execution	Independent executions
Reusability	Limited	Specialists reusable
Context passing	Built-in	Must manage explicitly

Recommendation: Start with AI Agent Tool for simpler systems. Move to sub-workflows when you need independent deployment, team ownership boundaries, or specialist reusability.

State Management Across Agents

In single-agent systems, state lives in memory. Multi-agent systems introduce a challenge: how do agents share context without losing information during handoffs?

Why State Management Matters

Consider a customer support scenario. The classifier routes a request to the Orders Agent. The Orders Agent checks the order status and finds the customer needs a refund. Refunds are handled by the Returns Agent. How does the Returns Agent know what the Orders Agent learned?

Without proper state management:

• Information gets lost between agent handoffs
• Agents ask customers to repeat information
• Debugging becomes impossible (what did each agent know?)
• Retry logic fails because intermediate state is gone

External State Storage

The production solution is external state storage. Store conversation context, intermediate results, and agent decisions in a database that all agents can access.

Supabase/PostgreSQL pattern:

// Before agent processes request
const state = await getState(sessionId);

// After agent completes
await updateState(sessionId, {
  lastAgent: 'orders',
  orderStatus: result.status,
  needsRefund: result.refundRequired,
  timestamp: new Date().toISOString()
});

Redis pattern for high-performance:

// Quick state retrieval
const state = JSON.parse(await redis.get(`session:${sessionId}`));

// State update with TTL
await redis.setex(
  `session:${sessionId}`,
  3600, // 1 hour TTL
  JSON.stringify(newState)
);

Idempotency for Safety

Multi-agent systems can fail mid-execution. When you retry, you don’t want agents to repeat actions they already completed. This is a core principle in agentic workflow design. Idempotency keys solve this.

Pattern:

// Check if action already completed
const actionKey = `${sessionId}:${agentName}:${actionType}`;
const alreadyDone = await checkAction(actionKey);

if (alreadyDone) {
  return previousResult;
}

// Execute action
const result = await executeAction();

// Mark as completed
await markComplete(actionKey, result);
return result;

This pattern ensures that even if the workflow restarts, agents don’t send duplicate emails, create duplicate refunds, or repeat any action that should only happen once.

Session Continuity

For conversational systems, you need to maintain context across agent boundaries. The session ID is your anchor.

Best practices:

• Generate session IDs at conversation start
• Include session ID in every agent call
• Store agent handoff history in state
• Use consistent session ID format across all agents

// Session ID format
const sessionId = `${userId}_${conversationId}_${timestamp}`;

// State structure
{
  sessionId: "user123_conv456_1703001234",
  history: [
    { agent: "classifier", decision: "orders", timestamp: "..." },
    { agent: "orders", action: "lookup", result: {...}, timestamp: "..." }
  ],
  currentContext: {...}
}

For database setup, see our Postgres setup guide.

Cost Optimization Strategies

Multi-agent systems can be expensive. Every agent call means LLM tokens. Orchestrators add overhead. Without optimization, costs spiral quickly.

Model Selection Per Role

Not every agent needs the same model. Match model capability to task complexity.

Agent Role	Recommended Model	Why
Orchestrator/Supervisor	Most capable (GPT-4, Claude 3)	Complex routing decisions
Classifiers	Fast/cheap (GPT-3.5, Haiku)	Simple categorization
Data retrieval	Medium capability	Needs to understand queries
Formatters	Cheapest available	Straightforward transformation

Savings example:

A system processing 10,000 requests/day with all agents using GPT-4 might cost $500/day. The same system with role-appropriate models might cost $150/day. That’s 70% savings without sacrificing quality where it matters.

Token Budgeting

Set explicit limits on each agent’s token consumption:

// In agent configuration
{
  maxTokens: 500,  // Limit response length
  maxIterations: 3 // Limit reasoning loops
}

Track token usage across your system:

// Log token usage per agent
const usage = {
  orchestrator: response.usage.total_tokens,
  timestamp: new Date().toISOString(),
  sessionId: sessionId
};
await logUsage(usage);

Caching Common Queries

Many requests are variations of the same question. Cache responses to avoid repeated LLM calls.

Implementation pattern:

// Generate cache key from normalized query
const cacheKey = normalizeQuery(userQuery);
const cached = await cache.get(cacheKey);

if (cached && !isExpired(cached)) {
  return cached.response;
}

// Process with agent
const response = await agent.process(userQuery);

// Cache for future requests
await cache.set(cacheKey, {
  response: response,
  timestamp: Date.now()
}, TTL);

Cache hit rates of 30-50% are common for support systems with frequently asked questions.

Cost Monitoring

You can’t optimize what you don’t measure. Implement cost tracking from day one.

Key metrics to track:

• Cost per agent per day
• Cost per conversation
• Token usage by agent type
• Cache hit rate
• Error rate (failed calls still cost tokens)

For rate limiting strategies, see our API rate limits guide.

Real-World Examples

These examples show how the patterns work in practice. Each includes the architecture and key configuration highlights.

Example 1: Customer Support System

Pattern: Supervisor with specialist delegation

Architecture:

[Chat Trigger] → [Supervisor Agent]
                    ├→ [Orders Tool Agent]
                    ├→ [Products Tool Agent]
                    └→ [Returns Tool Agent]

Supervisor prompt:

You are a customer support coordinator for TechStore.

Analyze each customer request and delegate to the appropriate specialist:
- orders: Order status, shipping, delivery issues
- products: Product information, compatibility, features
- returns: Refunds, exchanges, return shipping

Always confirm you've addressed the customer's complete request before ending.

Orders specialist prompt:

You handle order-related inquiries for TechStore.

Tools available:
- order_lookup: Find order by ID or email
- shipping_status: Check carrier tracking

Always verify customer identity before sharing order details.
If order requires a refund, recommend escalating to returns specialist.

Key insight: The supervisor doesn’t handle requests directly. It coordinates. Each specialist has focused tools and a narrow prompt, making them more reliable than one agent with all capabilities.

Example 2: Content Pipeline

Pattern: Sequential pipeline

Architecture:

[Trigger] → [Research Agent] → [Analysis Agent] → [Writer Agent] → [Editor Agent] → [Output]

Research agent prompt:

You are a research specialist gathering information for content creation.

Given a topic, use web search to find:
- Key facts and statistics (with sources)
- Expert opinions
- Recent developments
- Common questions people ask

Output structured research notes, not finished content.

Writer agent prompt:

You are a content writer creating articles from research.

Input: Research notes from the research specialist
Task: Transform notes into engaging article draft

Guidelines:
- Start with a compelling hook
- Use short paragraphs
- Include data and quotes from research
- Write for scanning (headers, bullets)

Key insight: Each agent in the pipeline has a specific output format that the next agent expects. Research outputs notes. Writer outputs draft. Editor outputs final content. Clear handoff formats prevent confusion.

Example 3: E-commerce Assistant

Pattern: Parallel with aggregation

Architecture:

[User Query] → [Dispatcher]
                  ├→ [Inventory Agent] ─┐
                  ├→ [Recommendations Agent] ─┼→ [Aggregator] → [Response]
                  └→ [Pricing Agent] ─┘

Dispatcher logic (n8n Code node):

// Determine which agents to call based on query
const agents = [];

if (query.includes('stock') || query.includes('available')) {
  agents.push('inventory');
}
if (query.includes('recommend') || query.includes('suggest')) {
  agents.push('recommendations');
}
if (query.includes('price') || query.includes('cost')) {
  agents.push('pricing');
}

// Default to all if unclear
if (agents.length === 0) {
  agents.push('inventory', 'recommendations', 'pricing');
}

return { agents, query };

Aggregator prompt:

Combine specialist responses into one coherent answer.

Inventory response: {{ $json.inventory }}
Recommendations response: {{ $json.recommendations }}
Pricing response: {{ $json.pricing }}

Create a unified response that addresses all aspects the customer asked about.

Key insight: Parallel execution makes this fast despite using three agents. The aggregator’s job is synthesis, not processing. This pattern works when specialists can work independently on the same request.

For more complex workflow architectures, our workflow development services can help design the right solution.

Error Handling and Production Monitoring

Multi-agent systems have more failure points than single agents. Production readiness requires systematic error handling.

Circuit Breakers

When an agent repeatedly fails, stop sending it traffic. This prevents cascading failures and saves costs on doomed requests.

Implementation pattern:

// Circuit breaker state
const breaker = {
  failures: 0,
  lastFailure: null,
  state: 'closed' // closed, open, half-open
};

// Before calling agent
if (breaker.state === 'open') {
  if (Date.now() - breaker.lastFailure > COOLDOWN) {
    breaker.state = 'half-open';
  } else {
    return fallbackResponse();
  }
}

// After agent call
if (error) {
  breaker.failures++;
  breaker.lastFailure = Date.now();
  if (breaker.failures >= THRESHOLD) {
    breaker.state = 'open';
  }
} else if (breaker.state === 'half-open') {
  breaker.state = 'closed';
  breaker.failures = 0;
}

Fallback Strategies

Every agent should have a fallback when it can’t complete a request:

Graceful degradation:

// If specialist agent fails
if (agentError) {
  return {
    response: "I'm having trouble accessing that information right now. " +
              "Let me connect you with a human specialist.",
    fallback: true,
    originalError: agentError.message
  };
}

Escalation path:

// If multiple retries fail
if (retryCount >= MAX_RETRIES) {
  await createSupportTicket({
    query: originalQuery,
    error: lastError,
    agentHistory: executionLog
  });

  return {
    response: "I've created a support ticket for you. " +
              "A specialist will follow up within 24 hours.",
    ticketId: ticket.id
  };
}

Logging Across Boundaries

With multiple agents, you need correlation IDs to trace requests through the system:

// At request start
const correlationId = generateCorrelationId();

// Every agent logs with same ID
logger.info({
  correlationId,
  agent: 'orders',
  action: 'lookup',
  input: sanitizedInput,
  output: sanitizedOutput,
  duration: endTime - startTime,
  tokens: response.usage.total_tokens
});

What to log:

• Request arrival (with full input)
• Routing decisions (which agent, why)
• Agent inputs/outputs (sanitize sensitive data)
• Errors with full context
• Timing and token usage

Production Metrics

Track these metrics to catch problems early:

Metric	Warning Threshold	Action
Agent error rate	> 5%	Check logs, consider circuit breaker
P95 latency	> 30 seconds	Optimize or add timeout
Cost per request	> baseline 2x	Audit token usage
Cache hit rate	< 20%	Review caching strategy

For timeout configuration, see our timeout troubleshooting guide.

Common Mistakes and How to Avoid Them

These mistakes appear repeatedly in multi-agent implementations. Learn from others’ pain.

Over-Orchestration

Mistake: Building multi-agent systems when a single agent would suffice.

Symptoms:

• Added complexity without added capability
• Higher costs than necessary
• Slower responses due to coordination overhead

Fix: Apply the decision framework from earlier. If a single agent with 5 tools handles your use case, don’t add orchestration just because it seems more sophisticated.

Tool Proliferation

Mistake: Giving each specialist agent too many tools, recreating the original problem.

Symptoms:

• Specialists become unreliable
• “Just add another tool” becomes the default solution
• Tool overlap between agents causes confusion

Fix: Strict tool limits per agent (3-5 max). If a specialist needs more tools, split it into two specialists.

Inconsistent Prompts

Mistake: Different tones, formats, or expectations across agents.

Symptoms:

• Jarring transitions when agents hand off
• Customers notice they’re talking to “different” assistants
• Aggregation becomes difficult

Fix: Create a prompt style guide. Define consistent persona, terminology, and response formats. Use templates to ensure consistency.

Memory Leaks in Long Conversations

Mistake: Unbounded memory growth as conversations continue.

Symptoms:

• Costs increase dramatically for long sessions
• Context windows overflow
• Old irrelevant context confuses agents

Fix: Implement memory windowing. Keep last N messages or last M tokens. Summarize older context rather than storing verbatim.

Shared Mutable State

Mistake: Multiple agents modifying the same state without coordination.

Symptoms:

• Race conditions cause data corruption
• Results vary unpredictably
• Debugging impossible

Fix: Either make state immutable (each agent creates new state) or implement proper locking when multiple agents might access simultaneously.

For debugging complex multi-agent issues, our workflow debugger tool can help isolate problems.

Frequently Asked Questions

When should I use multiple agents vs one agent with more tools?

Use multiple agents when you hit these thresholds:

• More than 6 tools needed - Split by domain
• Unrelated capabilities - Separate concerns
• Different model requirements - Use appropriate models per task
• Team boundaries - Let different teams own different agents

Stick with a single agent when:

• All tools relate to one domain
• Tool count is under 6
• Debugging a single agent is manageable
• Costs are acceptable

The overhead of orchestration only pays off when single-agent complexity becomes the bottleneck.

---

How do I share context between agents without losing information?

Three strategies work:

1. Explicit state passing: Store context in a database (Postgres, Redis) keyed by session ID. Each agent reads state at start, writes updates at end.

2. Context in handoff: When Agent A calls Agent B, include relevant context in the call. Agent A’s tool description for Agent B should specify what context to pass.

3. Shared memory: Use the same memory configuration (like Postgres Chat Memory) across agents with consistent session IDs. All agents see the same conversation history.

The explicit state approach is most reliable for complex systems. It creates a clear audit trail and survives agent restarts.

---

What’s the cost difference between single and multi-agent systems?

Multi-agent systems typically cost 20-100% more than equivalent single-agent systems due to:

• Orchestrator overhead (extra LLM call per request)
• Less efficient batching
• Duplicate context across agents

However, multi-agent systems can be optimized to cost less than overloaded single agents:

• Use cheaper models for simple specialist tasks
• Cache common queries (30-50% hit rates are achievable)
• Parallelize to reduce per-request costs through better model utilization

Net impact depends on implementation. A well-optimized multi-agent system can cost similar to a single agent while being far more reliable and maintainable.

---

How do I debug when agents aren’t delegating correctly?

Systematic approach:

1. Check orchestrator logs - What routing decision did it make? Was the classification correct?

2. Review tool descriptions - Vague or overlapping descriptions confuse routing. Each specialist should have clear, distinct triggers.

3. Test specialists independently - Send the same input directly to the specialist (bypassing orchestrator). Does it work in isolation?

4. Add classification confidence - Have the orchestrator output its confidence level. Low confidence suggests unclear request or overlapping specialist domains.

5. Trace correlation IDs - Follow the request through every agent. Where did information get lost or transformed incorrectly?

For complex debugging, consider adding a dedicated logging workflow that captures every agent interaction for replay and analysis.

---

Can I mix different LLM providers in a multi-agent system?

Yes, and it’s often a good strategy. Each n8n AI Agent node connects to its own chat model sub-node. You can configure:

• Orchestrator with Claude (strong reasoning)
• Classification with GPT-3.5-turbo (fast, cheap)
• Code specialists with Anthropic Opus (coding strength)
• Creative tasks with GPT-4 (variety)

Considerations:

• Different providers have different rate limits
• Latency varies between providers
• Some providers are better for certain tasks
• Cost structures differ (token vs character pricing)

The official n8n LangChain documentation covers multi-provider configuration in detail.

---

Moving Forward

Multi-agent orchestration transforms what’s possible with AI automation. But it’s not magic. Success comes from:

• Right-sizing complexity - Use orchestration when single agents fail, not before
• Clear boundaries - Each agent has one job it does well
• Robust state management - Shared context without shared problems
• Systematic monitoring - Catch issues before users do

Start with the simplest pattern that might work. Add complexity only when you’ve proven you need it. Every additional agent is a potential failure point and a cost multiplier.

For complex implementations, our n8n consulting services provide architectural guidance. For hands-on development, our workflow development team builds production-ready multi-agent systems.

The architecture patterns, implementation approaches, and error handling strategies in this guide give you the foundation. The rest is experimentation, measurement, and iteration.