Designing Scalable Multi-Agent AI Systems: Leveraging Domain-Driven Design and Event Storming

Multi-Agent AI Systems (MAS) are becoming increasingly crucial for tackling complex, real-world problems. With projections indicating that 82% of organizations plan to integrate AI agents and 25% of enterprises deploying them by 2025, it's essential to have robust methodologies for designing these systems. In this post, we’ll look at how blending Event Storming with Domain-Driven Design (DDD) can help build more effective and well-structured Multi-Agent Systems.

Understanding the Core Concepts

Multi-Agent AI Systems (MAS)

Multi-Agent AI Systems are computerized systems composed of multiple interacting intelligent agents. They are designed to solve problems that are difficult or impossible for individual agents or monolithic systems to address. Key characteristics of MAS include:

1. Autonomy of agents
2. Local views (no agent has full global knowledge)
3. Decentralization
4. Self-organization and self-direction

MAS can be applied to various domains, including autonomous driving, multi-robot factories, automated trading, and commercial games.

Domain-Driven Design (DDD)

Domain-Driven Design (DDD) is a software development approach focused on building a model that closely reflects the business domain. It aims to:

1. Align with how domain experts think about the problem
2. Build a common language that both developers and domain experts understand
3. Embed this shared language into the code to shape the domain model
4. Protect the core business logic from technical details and infrastructure noise

DDD focuses on modeling the business domain clearly and accurately, leading to software that's expressive, encapsulated, and easier to test, scale, and maintain.

Event Storming

Event Storming is a collaborative workshop-based method used to quickly explore and understand the domain of a software program. Key aspects include:

1. Lightweight approach requiring no computer support
2. Uses sticky notes on a wide wall to represent domain events
3. Brings together developers and domain experts
4. Focuses on identifying key domain events, commands, and aggregates to structure the system around meaningful business behavior.

Event Storming is particularly useful for business process modeling and requirements engineering, helping teams gain a shared understanding of the system's behavior.

The Rising Popularity of Multi-Agent AI Systems

Several factors contribute to the increasing adoption of MAS:

1. Increased Complexity: MAS can handle intricate, multifaceted challenges by distributing tasks among specialized agents.
2. Robustness: The distributed nature of MAS provides higher fault tolerance.
3. Collaborative Problem-Solving: MAS excel at tasks requiring diverse perspectives or parallel processing.
4. Adaptability: Individual agents in MAS can respond independently to changes.

The Challenge: Designing Complex Multi-Agent Systems

While Multi-Agent Systems offer numerous advantages, they present a significant design challenge. As the number of agents and their interactions grow, designing a coherent and efficient system becomes increasingly difficult. Traditional design approaches often struggle to capture the intricate relationships, behaviors, and communications between multiple autonomous agents. This complexity manifests in several ways:

1. Defining Agent Boundaries: Determining the responsibilities and scope of individual agents within the larger system.
2. Modeling Complex Interactions: Capturing the nuanced ways agents communicate and influence each other.
3. Maintaining System Coherence: Ensuring that the collective behavior of agents aligns with overall system goals.

Addressing Design Complexity in Multi-Agent Systems with DDD and Event Storming

Domain-Driven Design (DDD) and Event Storming offer powerful methodologies to address the mentioned design complexities of Multi-Agent Systems (MAS):

Domain-Driven Design Solutions:

• Bounded Contexts: Manage complexity by breaking the system into clearly defined, independent domains with their own models and language.
• Ubiquitous Language: Ensure shared understanding of system goals and functions.
• Rich Domain Model: Encapsulate business logic for robust, adaptable agents.

Event Storming Contributions:

• Collaborative Discovery: Map out system events and processes with domain experts and developers.
• Event-Driven Architecture: Align with MAS communication and coordination needs.
• Visual Modeling: Improve collaboration between AI developers, domain experts, and stakeholders through shared visual representations of the system."

Synergy of MAS, DDD, and Event Storming:

• Complementary Focus: MAS supplies the agent-based architecture, DDD brings domain modeling practices, and Event Storming supports collaborative domain exploration.
• Aligned Concepts: DDD's bounded contexts define agents, Event Storming identifies key events, and the event-driven nature of MAS aligns with both approaches.
• Scalable Architecture: DDD's modular approach translates well to scalable multi-agent systems.
• Domain Alignment: Keeps the MAS tightly synced with the real-world business domain.

By combining these approaches, developers can create well-structured, scalable multi-agent systems that closely align with domain models and business processes, leveraging the strengths of each methodology.

Applying the Approach: Workflow

Let's walk through a sample workflow for designing a complex Multi-Agent System using Event Storming and Domain-Driven Design:

1. Preparation: Gather a diverse team including domain experts, developers, and AI specialists. Prepare a large workspace with ample wall space for sticky notes.
2. Domain Event Identification: Represent key domain events using orange sticky notes.
3. Event Sequencing: Arrange events chronologically, using swim lanes for parallel processes.
4. Command Identification: Use blue sticky notes for commands that trigger events, categorizing them as user-initiated, system-triggered, policy-driven, or invariant-enforcing.
5. Establishing Boundaries: Apply the rule of cohesion to group related commands, events, and views into modules or bounded contexts.
6. Agent Identification: Use yellow sticky notes to represent agents responsible for executing commands or generating events.
7. Bounded Context Definition and Agent Communication Patterns: Use pink sticky notes to group related agents, commands, and events into clear areas. Then, use purple sticky notes to show the messages or events that connect these groups. This step results in a comprehensive MAS architecture that outlines each context, its agents, their responsibilities, and the events they handle. 

A Sample Use Case: AI-Driven Supply Chain Management System

To demonstrate the approach, let's walk through a sample workflow for designing a complex Multi-Agent System using Event Storming and Domain-Driven Design.

Sample Use Case: Initial Problem Statement

A global manufacturing company wants to create an intelligent supply chain management system that can handle procurement, production planning, inventory management, logistics, and customer order fulfillment across multiple countries and product lines.

Challenges Without DDD and Event Storming

Without a structured approach, the team might struggle with:

1. Defining agent boundaries and responsibilities
2. Managing complex interactions between different parts of the supply chain
3. Integrating with existing systems and external partners
4. Handling different regulations and practices across countries
5. Balancing local optimization with global efficiency

Applying Event Storming and DDD

To demonstrate how Event Storming and Domain-Driven Design (DDD) can be applied to design a Multi-Agent System (MAS), we will use color-coded sticky notes to represent different elements in the system.

Step 1: Preparation

• A diverse team including domain experts, developers, and AI specialists.
• A large workspace with ample wall space for sticky notes.
• Materials: sticky notes in multiple colors

Step 2: Domain Event Identification

Domain events are significant occurrences in the system that describe what has happened. These are written using past-tense verbs.

Examples:

• "Raw Material Ordered"
• "Shipment Delayed"
• "Production Batch Completed"
• "Customer Order Received"
• "Inventory Level Critical Reached"
• "Discount Applied"
• "Discount Rejected"

Visual representation:

Step 3: Event Sequencing

Arrange the events chronologically from left to right on the wall. Use swim lanes to represent parallel processes:

Example:

• [Raw Material Ordered] -> [Raw Material Received] -> [Production Batch Started] -> [Production Batch Completed] -> [Product Shipped] -> [Product Delivered]
• [Customer Order Received] -> [Order Validated] -> [Discount Validated] -> [Order Fulfilled] -> [Invoice Sent]
• [Inventory Level Critical Reached] -> [Reorder Point Triggered] -> [Raw Material Ordered]

Visual representation:

Step 4: Command Identification

Commands are actions that can trigger domain events. They can be initiated by users, external systems, or as a result of policies. Let's categorize them:

1. User-Initiated Commands: These are actions directly triggered by human users interacting with the system. For example:

   • [Place Raw Material Order]

   • [Start Production Batch]

   • [Ship Product]

2. System-Triggered Commands: These are automated actions initiated by the system itself in response to certain conditions or events. For instance:

   • [Send Invoice] (Automatically triggered after order fulfillment)

   • [Update Inventory] (Executed when new stock arrives)

3. Policy-Driven Commands: These are actions that occur based on predefined business rules or policies within the system. For example:

   • [Trigger Reorder Point] (Automatically orders more stock when inventory is low)

   • [Apply Bulk Discount] (Automatically applies a discount for large orders)

4. Invariant-Enforcing Commands: These ensure that certain conditions or rules always hold true in the system. For instance:

   • [Validate Order Total] (Ensures the order meets minimum amount for processing)

   • [Check Credit Limit] (Verifies customer hasn't exceeded their credit limit)

Examples:

• [Place Raw Material Order] (User) -> [Raw Material Ordered]
• [Receive Raw Material] (System) -> [Raw Material Received]
• [Trigger Reorder Point] (Policy) -> [Reorder Point Triggered]
• [Monitor Inventory Levels] (Policy) -> [Inventory Level Critical Reached]
• [Start Production Batch] (User) -> [Production Batch Started]
• [Complete Production Batch] (System) -> [Production Batch Completed]
• [Create Customer Order] (User) -> [Customer Order Received]
• [Validate Order Total] (Invariant) -> [Order Validated]
• [Apply Discount] (Invariant) -> [Discount Validated]
• [Fulfill Customer Order] (User) -> [Order Fulfilled]
• [Ship Product] (User) -> [Product Shipped]
• [Deliver Product] (System) -> [Product Delivered]
• [Send Invoice] (System) -> [Invoice Sent]

Visual representation:

Step 5: Establishing Boundaries

To define boundaries between modules, we apply the rule of cohesion: Things that change together and are used together should be kept together. For our supply chain management system, we can establish the following boundaries:

Supply Chain Context:

• Commands: [Place Raw Material Order], [Receive Raw Material], [Trigger Reorder Point], [Monitor Inventory Levels], [Update Inventory]
• Events: [Raw Material Ordered], [Raw Material Received], [Reorder Point Triggered], [Inventory Level Critical Reached], [Inventory Updated]

Production Context:

• Commands: [Start Production Batch], [Complete Production Batch]
• Events: [Production Batch Started], [Production Batch Completed]

Order Fulfillment and Logistics Context:

• Commands: [Create Customer Order], [Fulfill Customer Order], [Ship Product], [Deliver Product], [Send Invoice], [Validate Order Total], [Apply Discount]
• Events: [Customer Order Received], [Order Validated], [Discount Validated], [Order Fulfilled], [Product Shipped], [Product Delivered], [Invoice Sent]

Visual Representation: 

Note: The sequence of events and commands often spans across multiple contexts. This is expected because real-world business processes frequently involve interactions between different domains or departments. For example, a customer order (Order Fulfillment context) may trigger production (Production context) and affect inventory levels (Supply Chain context). This cross-context interaction reflects the interconnected nature of business operations and highlights the importance of clear boundaries and well-defined interfaces between contexts to manage complexity and ensure smooth system operation.

Step 6: Agent Identification

Now that our contexts are set, we can pinpoint the agents in each one who handle commands or trigger events:

Supply Chain Context:

• Supply Chain Agent
• Inventory Management Agent

Production Context:

• Production Agent

Order Fulfillment and Logistics Context:

• Order Fulfillment Agent
• Logistics Agent
• Invoicing Agent
• Order Validation Agent
• Discount Management Agent

Visual Representation:

Step 7: Resulting MAS Architecture

Now that we have identified our contexts and agents, we can define their responsibilities more precisely. This step naturally leads to the creation of a comprehensive table that outlines each context, its agents, their responsibilities, and the events they handle.

Future Considerations for Scaling and Optimizing Multi-Agent Systems

As the Multi-Agent System (MAS) grows in complexity and scale, consider the following strategies to ensure its continued effectiveness and efficiency:

Scaling the MAS Architecture

1. Hierarchical Agents: Implement supervisor agents to manage groups of lower-level agents, creating a more organized structure1.
2. Dynamic Agent Creation: Design a system that can create and destroy agents as needed, allowing for flexible resource allocation.
3. Load Balancing: Distribute events across multiple instances of an agent type to prevent bottlenecks and ensure optimal performance.
   
   

Comprehensive Testing and Validation

Establish a robust testing pipeline that includes:

• Unit Testing: Verify individual agent behaviors
• Integration Testing: Test how agents work together inside a bounded context.
• System Testing: Simulate complex scenarios involving multiple bounded contexts
• Chaos Engineering: Introduce controlled failures to test system resilience
  
  

Best Practices for MAS Design

1. Domain Event Focus: Highlight important business events and ignore technical noise.
2. Agent Granularity: Keep agents focused with a clear, single responsibility, avoiding being too detailed or too broad.
3. Bounded Context Definition: Base boundaries on linguistic and functional cohesion, promoting shared language and related functionalities within contexts.
4. Communication Patterns: Explore various patterns such as publish-subscribe, request-response, or event sourcing for efficient inter-agent communication.
5. Scalability-Oriented Design: Create a flexible architecture that allows for easy addition of new agents or expansion of existing capabilities.
6. Conflict Resolution Mechanisms: Establish clear hierarchies or negotiation protocols to manage potential conflicts between agents.
7. State Management: Decide how agents keep track of their state and share updates with others.
8. Robust Error Handling: Specify agent behaviors for handling failures and unexpected events.
9. Learning and Adaptation: Consider integrating machine learning capabilities to enhance agent decision-making over time.
10. Security and Privacy: Keep sensitive data protected inside each context and secure the communication between agents.

By incorporating these considerations into your MAS design and development process, you can create a more scalable, resilient, and adaptable system capable of handling increasing complexity and evolving business requirements.

Challenges and Limitations

While combining Domain-Driven Design (DDD) and Event Storming for Multi-Agent Systems (MAS) offers numerous benefits, there are scenarios where this approach may not be optimal:

1. For deterministic components, traditional non-AI solutions are often more appropriate. A hybrid approach can be taken, using conventional algorithms for predictable parts and MAS for adaptive decision-making.
2. For simpler MAS, this approach can introduce unnecessary complexity, potentially leading to overengineering and increased development time.
3. Establishing clear boundaries between agents can be challenging, especially when agents have overlapping responsibilities or need to collaborate closely. To address this, consider implementing a hierarchical structure with a master alignment system overseeing sub-agents with limited autonomy, ensuring collective behavior remains aligned while maintaining clear boundaries.

By carefully considering these limitations and tailoring the approach to your specific needs, you can maximize the benefits of using DDD and Event Storming for Multi-Agent Systems while mitigating potential drawbacks.

Conclusion

The integration of Event Storming and Domain-Driven Design (DDD) offers a powerful framework for designing Multi-Agent AI Systems (MAS). This approach provides several benefits:

1. Enhanced domain understanding through collaborative discovery
2. Scalable architecture using DDD's bounded contexts
3. Improved communication between technical and non-technical stakeholders
4. Flexibility to adapt to the dynamic nature of MAS
5. Coherent system design with clearly defined agent responsibilities

As we progress through 2025, this methodology will be crucial for organizations looking to leverage MAS for complex problem-solving. Future developments may include greater integration of machine learning, standardized inter-agent communication patterns, and increased focus on security in distributed AI systems.

By adopting this comprehensive approach, organizations can create well-structured, scalable multi-agent systems that closely align with their business domains, positioning themselves at the forefront of AI innovation.

References

1. Generative AI in organizations 2024 - Capgemini. (2025, February 13). Capgemini. https://www.capgemini.com/insights/research-library/generative-ai-in-organizations-2024/
2. Afshar, V. (2024, December 4). 25% of enterprises using AI will deploy AI agents by 2025. ZDNET. https://www.zdnet.com/article/25-of-enterprises-using-ai-will-deploy-ai-agents-by-2025/
3. Boyer, J. (n.d.). Event Storming - IBM Automation - Event-driven Solution - Sharing knowledge. https://ibm-cloud-architecture.github.io/refarch-eda/methodology/event-storming/
4. AI agent architecture and design. (n.d.). Deloitte United States. https://www2.deloitte.com/us/en/pages/consulting/articles/ai-agent-architecture-and-multiagent-systems.html
5. InfoWorld. (2025, January 28). A distributed state of mind: Event-driven multi-agent systems. InfoWorld. https://www.infoworld.com/article/3808083/a-distributed-state-of-mind-event-driven-multi-agent-systems.html
6. International Journal SSRG (2025, March 30) Designing Scalable Multi-Agent AI Systems: Leveraging Domain-Driven Design and Event Storming . https://www.internationaljournalssrg.org/IJCSE/paper-details?Id=588
7. Research Gate (2025, April 26) Designing Scalable Multi-Agent AI Systems: Leveraging Domain-Driven Design and Event Storming. https://www.researchgate.net/publication/391084293_Designing_Scalable_Multi-Agent_AI_Systems_Leveraging_Domain-Driven_Design_and_Event_Storming