Introduction
Getting multiple AI agents to work together at scale is one of the toughest challenges in modern engineering. Inspired by the experiences of Chase Roossin and Steven Kulesza from Intuit, this guide will walk you through the key steps to design a system where agents collaborate effectively, avoid conflicts, and scale gracefully. Whether you're building a customer support swarm or a complex automation pipeline, these principles will help you tame the chaos.
What You Need
- Clear understanding of each agent's role and capabilities
- A communication protocol (e.g., REST, gRPC, or event-driven messaging)
- Shared state or memory mechanism (database, in-memory cache, or distributed store)
- Conflict resolution logic (priority rules, negotiation patterns)
- Orchestration layer or middleware (like Kubernetes, Airflow, or custom coordinator)
- Monitoring and observability tools (logs, metrics, tracing)
- A testing and staging environment to simulate multi-agent interactions
Step-by-Step Guide
Step 1: Define Agent Roles and Boundaries
Start by clearly specifying what each agent is responsible for. Avoid overlapping capabilities that lead to redundant work or conflicts. For example, one agent could handle data retrieval, another performs analysis, and a third executes actions. Use a responsibility matrix to document who does what.
Create a contract for each agent: its inputs, outputs, success criteria, and error states. This makes it easier to reason about interactions and debug failures.
Step 2: Establish a Communication Protocol
Agents must talk to each other in a consistent language. Choose a protocol that suits your scale: asynchronous messaging (like Kafka or RabbitMQ) works well for decoupled systems, while synchronous APIs suit tightly coupled actions. Define a schema for messages (e.g., using JSON or protobuf) and include metadata like request IDs, timestamps, and priority.
Also decide on shared state access patterns: agents can push updates to a central store or pull only what they need. Use idempotent operations to handle retries gracefully.
Step 3: Implement a Shared State or Memory
Agents often need to share context, such as a customer's conversation history or a task's progress. Use a distributed store like Redis, DynamoDB, or a database that supports optimistic locking. Define a schema that includes version numbers or timestamps to prevent overwrites.
For real-time coordination, consider an event sourcing pattern where each change is recorded as a log. Then agents can replay events if they crash or restart. This also aids observability.
Step 4: Design Conflict Resolution Mechanisms
When two agents try to update the same resource simultaneously, you need a strategy. Options include:
- Last-write-wins (simple but risky).
- Priority-based (assign ranks to agents).
- Consensus algorithms (like Raft or Paxos) for critical updates.
- Human-in-the-loop for high-stakes decisions.
Test each scenario in your staging environment to ensure failures don't cascade.
Step 5: Scale with an Orchestration Layer
As you add more agents, coordination becomes exponential. Use an orchestrator that manages agent lifecycles, routing, and retries. Tools like Kubernetes handle container orchestration; for business logic, consider workflow engines (Temporal, Airflow) or custom dispatchers.
Orchestrators can also enforce policies like rate limiting, timeouts, and dead-letter queues for failed messages. This reduces the cognitive load on individual agents.
Step 6: Monitor, Log, and Iterate
Treat your multi-agent system as a distributed system. Collect logs from every agent, aggregate metrics (throughput, latency, error rates), and trace requests across agents. Use dashboards to spot bottlenecks or agent fights.
Run periodic chaos experiments (kill random agents, introduce latency) to test resilience. Use the insights to refine communication protocols or add fallback agents.
Tips for Success
- Start simple: With two agents and a basic handshake, then add complexity gradually.
- Embrace idempotency: Design agents to handle repeated messages without side effects.
- Test adversarial scenarios: Agents might compete for resources—write tests that simulate conflicts.
- Use circuit breakers: Prevents cascading failures when an agent is slow or down.
- Involve domain experts: Engineers alone can't define all agent roles; work with product owners.
- Document everything: Agent interfaces, state schemas, and conflict rules reduce future confusion.
By following these steps, you'll be well on your way to creating a multi-agent system that plays nice at scale—just like the experts at Intuit demonstrated.