The Limits of Single Agents
A single AI agent works great for focused tasks — answering support questions, booking appointments, qualifying leads. But real business workflows are rarely that simple.
Consider a customer service interaction that requires:
- Understanding the customer's issue (language comprehension)
- Looking up their account and order history (database queries)
- Checking inventory or availability (API calls)
- Making a decision about refund/replacement policy (business logic)
- Processing the action (payment system)
- Sending confirmation (communication)
- Updating internal records (CRM)
A single agent can technically do all of this. But as the task list grows, performance degrades. The agent's context window fills up, it loses focus, and it makes mistakes.
Multi-agent systems solve this by dividing complex workflows among specialized agents that collaborate.
What Is a Multi-Agent System?
A multi-agent system (MAS) is an architecture where multiple AI agents — each with a specific role — work together to accomplish complex tasks. Instead of one generalist, you have a team of specialists.
The market reflects the growing adoption: the multi-agent AI market is projected to reach $184.8 billion by 2034, growing at 45.8% annually.
Architecture Patterns
1. Orchestrator Pattern
A central "manager" agent receives requests and delegates subtasks to specialized agents.
User Request
↓
[Orchestrator Agent]
↓ ↓ ↓
[Research] [Action] [Communication]
Agent Agent Agent
Best for: Well-defined workflows with clear task boundaries. Customer support, order processing, intake workflows.
2. Pipeline Pattern
Agents are arranged in sequence. Each agent processes the output of the previous one.
Input → [Agent A] → [Agent B] → [Agent C] → Output
Extract Analyze Generate
Best for: Document processing, data enrichment, content generation pipelines.
3. Collaborative Pattern
Agents work as peers, sharing information and negotiating decisions. No single orchestrator.
[Agent A] ←→ [Agent B]
↕ ↕
[Agent C] ←→ [Agent D]
Best for: Complex decision-making where multiple perspectives matter. Research, strategic analysis, creative work.
4. Hierarchical Pattern
Multiple layers of orchestration. A top-level agent manages mid-level agents, which manage worker agents.
[Executive Agent]
↓ ↓
[Manager A] [Manager B]
↓ ↓ ↓ ↓
[W1] [W2] [W3] [W4]
Best for: Large-scale enterprise systems with many domains and subsystems.
When to Use Multi-Agent Systems
Not every AI project needs multiple agents. Use this decision framework:
| Factor | Single Agent | Multi-Agent | |--------|-------------|-------------| | Task complexity | Simple, linear | Multi-step, branching | | Number of tools | 1–5 | 6+ | | Domain expertise needed | One domain | Multiple domains | | Context window pressure | Low | High (too much for one agent) | | Reliability requirements | Moderate | High (need isolation) | | Scale | Hundreds of requests | Thousands+ per day |
The rule of thumb: if your single agent's prompt is getting unwieldy or its accuracy is dropping as you add more tools, it's time to split into multiple agents.
Real-World Example: Healthcare Intake System
Here's how we'd architect a multi-agent intake system for a healthcare clinic:
Receptionist Agent — Handles the initial patient interaction. Collects name, reason for visit, insurance info. Routes to specialized agents.
Scheduling Agent — Accesses the practice management system. Finds available slots. Books appointments. Handles rescheduling.
Insurance Agent — Verifies coverage. Checks eligibility. Estimates patient responsibility. Flags issues for human review.
Follow-Up Agent — Sends appointment confirmations. Handles reminders. Processes cancellations. Manages recall lists.
Escalation Agent — Monitors all interactions. Detects when a patient needs human attention (urgent medical concerns, complaints, complex issues). Routes to appropriate staff.
Each agent has its own tools, its own prompt, and its own guardrails. They communicate through a shared context layer and the orchestrator manages the flow.
Building Multi-Agent Systems: Key Decisions
Agent Communication
How do agents share information?
- Shared memory: A central data store that all agents read from and write to. Simple but can create contention.
- Message passing: Agents send structured messages to each other. More complex but better isolation.
- Event-driven: Agents emit events and subscribe to relevant topics. Scales well for large systems.
State Management
Multi-agent systems need shared state — but carefully managed:
- What's the current status of this workflow?
- What has each agent done so far?
- What information has been collected?
- What decisions have been made?
Use a structured state object that the orchestrator manages and agents can read/update within their scope.
Error Handling
When Agent B fails, what happens?
- Retry: Try again with the same or different parameters
- Fallback: Use a simpler approach or different agent
- Escalate: Route to a human for handling
- Compensate: Undo what previous agents did (critical for transactional workflows)
Design error handling before you build. It's 30% of the work and 80% of the reliability.
Cost Management
More agents means more API calls. Strategies to control costs:
- Use smaller, cheaper models for simple routing and classification agents
- Cache common queries and responses
- Set per-agent and per-workflow budget limits
- Monitor costs in real-time with alerts
Frameworks and Tools
Several frameworks support multi-agent development:
| Framework | Strengths | Best For | |-----------|----------|---------| | CrewAI | Simple role-based agents, easy setup | Small to medium teams of agents | | AutoGen | Microsoft-backed, flexible conversation patterns | Research and complex reasoning | | LangGraph | Graph-based workflows, strong state management | Production systems with complex flows | | Custom | Maximum control and optimization | Enterprise-grade, performance-critical systems |
For production systems, we typically recommend custom architectures built on proven foundations (NestJS, PostgreSQL, Redis for state) rather than relying entirely on frameworks that may not scale to your needs.
Getting Started
- Start with one agent — Get a single agent working well for your core workflow
- Identify the bottleneck — Where does the single agent struggle? Context overload? Too many tools? Multiple domains?
- Split strategically — Extract one specialization at a time. Don't go from 1 to 10 agents overnight.
- Build the orchestration layer — Define how agents communicate, share state, and handle errors
- Monitor everything — Multi-agent systems need more observability, not less
At Autor, we design and build multi-agent systems for businesses that have outgrown single-agent solutions. Let's architect your system.