Rethinking Workflows: How Choreography Empowers AI Agents to Collaborate Without a Boss

Picture a group of highly skilled professionals in one office. Each person is an expert in a particular domain—finance, marketing, customer service, logistics—yet there’s no single manager telling them what to do. Instead, they each listen for signals relevant to their specialty (for example, “a new sale happened” or “inventory is low”) and take action immediately. They pass on new information as events to others who might need it. This smooth, autonomous coordination feels almost magical because there’s no commanding figure in the middle.

That’s precisely how Multi-Agent Systems (MAS) operate when designed with a choreography pattern in an event-driven environment. There’s no single orchestrator dictating the sequence of tasks. Instead, each agent reacts to the events it cares about, carrying out its role and emitting new events for other agents to consume. This article explains why that matters and how to build such a system.

Why Move Beyond a Central Orchestrator?

Traditional orchestrated workflows rely on a master process that dictates each step. This might be fine for simpler pipelines, but it can quickly become a bottleneck as complexity grows.

• Single Point of Failure: If the orchestrator crashes, your entire flow halts.
• Scaling Challenges: You often have to scale the whole orchestrator, even if only one part of the workflow is under heavy load.
• Difficult to Evolve: Adding or changing a step means digging into the orchestration logic, which can be risky and time-consuming.

Choreography, by contrast, pushes decision-making to individual agents. Each agent knows which events to listen for and what to do when those events appear. The bigger “workflow” emerges naturally from these interactions—no single entity dictates the entire sequence.

A Quick Comparison: Workflows vs. Single AI Orchestrator vs. Choreographed MAS.

Ttraditional workflows, a single AI orchestrator, and a choreographed multi-agent system (MAS) differ in how they handle tasks and adapt to change.

1. Traditional Workflows

Traditional workflow automation tools like make.com, Zapier, or n8n rely on a predefined sequence of steps that are visually laid out in a flowchart. This approach is intuitive for stable or repetitive tasks, since the logic is easy to follow at a glance. However, the very structure that makes them clear and straightforward can become a limitation when new conditions appear or the process needs to adapt quickly, often requiring a complete redesign of the workflow diagram. Below are a few notable challenges:

• Rigid Sequencing: Adjusting the order of operations or adding new branches can be cumbersome.
• Complex Branching Logic: Multiple nested conditions or exceptions often lead to convoluted flows that are hard to maintain.
• Error Handling: Built-in exception management is often minimal, and debugging or retrying failed runs can be tedious.
• Collaboration and Version Control: Tracking changes over time and coordinating edits among team members can be difficult.
• Onboarding and User Adoption: While these tools aim to be user-friendly, the initial learning curve and the need for clear documentation can slow down team-wide adoption.

2. Single AI Orchestrator

Instead of a static diagram, you have an AI “boss” that evaluates new data and decides which tasks to run, possibly assigning sub-tasks to specialized tools or agents. It’s more flexible than a strict workflow, since the AI can change its plan based on evolving conditions. However, routing every decision through this orchestrator can lead to problems:

• Bottlenecks and Single Points of Failure: If the AI is overwhelmed or crashes, nothing moves forward. The entire process depends on this one decision-maker.
• Hallucinations or Bad Plans: An advanced AI might occasionally produce unrealistic or incorrect strategies—sometimes called “hallucinations”—especially if it has poor input data or misunderstood context. This can send the entire process down the wrong path.
• Overhead in Monitoring AI Decisions: Teams need to continuously validate the AI’s outputs or guard them with strict checks, which can be as time-consuming as maintaining a traditional workflow.
• Scaling Challenges: If task volume grows, the orchestrator must handle more requests, potentially limiting throughput unless the AI’s resource allocation also grows proportionally.
• Inflexibility in Rapid Task Hand-Off: The AI might assign sub-tasks correctly, but any changes to how it breaks down work requires updating or retraining the orchestrator’s logic, which can be slow if the AI is complex.

Choreographed, event-driven MAS removes the idea of one boss entirely. Each agent has its own job—like parsing input or running a query—and subscribes to events that might matter to it. When one agent completes a task, it emits an event, and any agent that cares about that event can act on it. This creates a system where many tasks can run in parallel, new agents can be added without rewriting a central plan, and failures in one agent don’t halt the entire process. However, it also means you need to define clear event names, manage errors carefully, and maintain logs or traces so you can see how events move from one agent to another.

The Choreographed, Event-Driven Approach

1. Agents as Autonomous Services: Each service (e.g., PaymentService, InventoryService, NotificationService) becomes an autonomous agent that manages its own logic and subscribes to events relevant to its domain.
2. Events Drive Collaboration: Agents publish events (like “OrderPlaced”) when they complete a step or detect a condition, and other agents listening for those events react accordingly. This creates a chain reaction of tasks without relying on a master process to coordinate them.
3. Resilience and Scalability: Because agents operate independently, failures in one agent don’t necessarily bring down the rest, and you can scale individual services based on their specific load.
4. Parallel Processing and Scalability:Because agents act independently, multiple tasks can run at once. If your system is under heavy load, you can scale up the agents that are busiest without touching the rest.
5. Loose Coupling: Agents only need to agree on event names and payload structures. They don’t need to be aware of each other’s internal implementations, which simplifies updates or replacements in the future.

Emergent Workflows

Because no single agent dictates the full process, your end-to-end workflow arises from multiple event exchanges.

Agents act when they see events they care about, then broadcast new events to share outcomes or request additional steps. If you need a new action—like sending a personalized email whenever an order is placed—you simply create or update an agent to subscribe to the “OrderCompleted” event and publish “EmailSent” once done. This incremental, loosely coupled method lets your processes adapt naturally to new requirements without requiring a full redesign.

Adding or Modifying Services

In a choreographed system, adding or modifying a service typically means subscribing to existing events or publishing new ones. For example, if you introduce a “RecommendationService” that suggests related items after a purchase, you can have it listen for “OrderCompleted” and emit “RecommendationsReady.” There’s no need to alter a master flow diagram or navigate complex conditional branches. Each agent’s responsibilities remain self-contained, and the rest of the system only needs to know about the new events if they want to use them.

Debugging and Logging

Rather than digging through a large workflow diagram, you can trace the flow of events to see which agent acted (or failed) at each step. A centralized event log or “event store” allows you to replay and analyze every published event, pinpointing exactly where an error occurred. Because each agent reports its own status, and events are the common thread linking them together, debugging often becomes more transparent. If one agent goes down, other agents can continue to function and queue up events until the failing component recovers.

Resilience and Scalability

When each agent runs independently, a failure in one service doesn’t halt the entire process. Agents can be scaled individually based on their specific workloads—if your “InventoryAgent” is getting hammered with requests, you can spin up more instances of just that agent. This means you avoid monolithic bottlenecks and keep the overall system responsive. Event-driven choreographies also support parallel processing: if two services both react to the same event, they can execute in tandem without waiting for a central coordinator.

Retries and Dead Letter Queues

In a choreographed, event-driven system, retries and dead letter queues (DLQs) help maintain reliability when an agent fails to process an event. If a message delivery or processing attempt exceeds its retry limit, it automatically moves to a DLQ for later inspection and potential reprocessing. This approach keeps the overall workflow from getting stuck on one problematic event, allowing the rest of the system to continue operating. Moreover, DLQs provide a clear record of failed events, making it easier to pinpoint errors, debug code, and refine agent logic without disrupting normal operations.

Choreography vs. Typical Pub/Sub

It’s easy to conflate choreographed systems with classic pub/sub. In pub/sub, a publisher sends messages to a topic, and any subscribers reading that topic receive those messages. This decouples producers and consumers, but doesn’t inherently create a multi-step process.

In a choreographed system, pub/sub is still the underlying communication mechanism. However, each service not only consumes events but also emits new ones when it completes tasks or encounters issues. The overall flow emerges from how these events link multiple agents’ actions. Think of it as pub/sub with embedded business logic that collectively forms a dynamic process.

Multi-Agent Systems: A Perfect Match

Multi-Agent Systems thrive on distributing intelligence across autonomous units (agents). Each agent focuses on its domain—like payments or shipping—and can make decisions without external instruction. When you layer this onto an event-driven, choreographed environment:

• Local Decision-Making: Agents don’t wait for instructions; they see an event, apply their logic, and do their job.
• Real-Time Collaboration: One agent’s success or failure instantly informs others. An agent that can’t complete a task might emit an event signaling a problem, prompting another agent to step in or retry.
• Scalable Growth: Adding a new agent is as simple as introducing another event subscriber. If you want a “RecommendationAgent,” for instance, you just have it listen for “OrderPlaced” and react accordingly.

A Real-World Design: Natural Language Query → SQL → Execution → Results

Below is a high-level illustration of how a choreographed, event-driven approach can power a Multi-Agent System for natural language queries that generate and run SQL, then return human-friendly results:

1. User Query in Natural Language

A user poses a question or request in plain English, such as “Show me total sales by region for last quarter.”

2. Communication Layer

• Receives the user query as an event (e.g., QueryReceived).
• Routes data among various agents (no single orchestrator).
• Logs these events to an Event Store for replay or auditing, using a technique like event sourcing.

3. SQL Agent

• Subscribes to QueryReceived.
• Analyzes the user’s natural language query alongside relevant schema (using retrieval-augmented generation or RAG).
• Emits SQLGenerated with the proposed SQL statement.
• If it encounters an error or needs more info, it publishes events like SQLGenerationError, prompting a retry or human-in-the-loop intervention.

4. SQL Runner

• Subscribes to SQLGenerated.
• Validates the query, checks security rules, and executes it on the SQL DB if approved.
• Publishes QuerySuccess or QueryError depending on the outcome.
• May also log certain events for auditing or pass them to a Retry or Validator agent if there are known fixable issues.

5. Integration Agent

• Listens for QuerySuccess to aggregate results, relevant schema info, and any additional metadata.
• Collates everything into a structured response (e.g., JSON with data rows, column names).

6. UI Agent

• Subscribes to integrated results (e.g., ResultsReady).
• Converts the returned dataset into UI-friendly HTML or a chart.
• Displays it to the user in the chat or dashboard.

7. Event Store

• Logs the entire process (e.g., QueryReceived, SQLGenerated, QuerySuccess) for auditing, debugging, or replay in case of system restarts.

Because each agent acts upon events relevant to its domain, they collectively form a pipeline without needing a central orchestrator. If the SQL Agent fails, other agents continue running, and the system can simply re-route or retry once the SQL Agent recovers.

The Power of No Single Boss

Building a Multi-Agent System with a choreographed, event-driven design keeps things flexible, scalable, and resilient. You remove single points of failure, allow each service to scale on its own, and add new features simply by introducing more event-driven agents. While AI planners and executors can bring advanced capabilities, think about whether they need to be centralized or if they can operate like any other agent in the system.

If you’re aiming for distributed intelligence—whether that’s turning natural language into SQL or managing deliveries—this choreographed approach is a strong alternative to a rigid orchestrator. Each agent freely acts, emits events, and hands off tasks without waiting for approval from a single controller. The result is a self-organizing network that adapts on its own, ready for both routine and unexpected demands in a smooth, scalable way.