Introduction
Last updated: 2025-05-16
Generative AI is shifting rapidly from research to production, with enterprises seeking robust, maintainable and scalable solutions to solve complex problems. In this landscape, the design of multi-agent systems, where numerous specialized AI agents cooperate to solve problems, has become critically important: enables modularity, domain expertise, and agility, offering adaptability as business needs and AI capabilities evolve.
These systems reflect the natural structure of organizations: different roles, responsibilities, and domains mapped to individual or composite agents, each optimized for specific knowledge or workflows.
Design Principles
The following design principles are especially crucial for multi-agent systems, as identified through real-world implementations in large enterprises. These principles address challenges unique to environments where multiple agents interact, collaborate, and exchange information. Treat them as guiding foundations rather than rigid requirements:
This repository is continuously maintained by contributors to provide the community with the most up-to-date guidance, grounded in real-world enterprise experience and aligned with the latest developments in multi-agent systems. We welcome your feedback, suggestions, and references to additional resources that could enrich this documentation. As we evolve this reference architecture, community input plays a vital role in shaping its relevance and impact.
| Name | Description |
|---|---|
| Separation of Concerns | In multi-agent systems, it’s essential that each agent has a distinct and well-defined responsibility. This clarity enables focused development, scalable deployment, reduced cross-cutting changes, and makes it possible for agents to encapsulate deep domain expertise. |
| Secure by Design | With multiple agents communicating and acting within the same ecosystem, robust security is paramount. This includes strict authentication, authorization, and policy enforcement for every agent interface. It’s also necessary to carefully manage sensitive data flows between agents, minimize data retention, and clearly define data handling practices to prevent unintended leakage or escalation of privilege. |
| Observability & Traceability | Multi-agent workflows generate complex, interdependent behaviors. Agents should be instrumented so their actions, data exchanges, and decisions can be traced end-to-end across the system using common identifiers and correlated metrics. This level of observability enables efficient troubleshooting, auditing, and deep understanding of system operations—far beyond what is required in single-agent scenarios. |
| Agent Registration & Lifecycle Governance | Agents should be explicitly registered, versioned, and validated before being included in production environment. Registration should capture agent capabilities, security posture, and lifecycle state to prevent duplication, control upgrades, and reduce risks from rogue or malfunctioning agents. |
| Failure Isolation & Graceful Degradation | Failures in one agent should not cascade to others. Consider fallback mechanisms, retries, or degraded modes of operation to ensure the workflow can continue, even in the presence of partial failures. |
| Context Management | Establish clear rules and policies regarding what context or conversational state is shared among agents and for how long. Carefully control context propagation to avoid privacy issues, data leakage, or logic confusion. |