Organizations which design systems (in the broad sense used here) are constrained to produce designs which are copies of the communication structures of these organizations..
Background
In the first quarter of 2026, AI reached a true inflection point in its societal impact. Much of this momentum can be attributed to the continuous productization and narrative-building efforts by OpenAI and Anthropic. This combination has made AI significantly more accessible, enabling people to directly leverage it to accomplish tasks—an undeniably important step forward.
However, as with any technological wave, rapid adoption often comes with a loss of perspective. Maintaining clarity becomes even more critical in enterprise digitalization, where the stakes are higher and the systems more complex.
Collaboration between human-and-agent as well as between agents is widely seen as the next frontier. Tools like minus, openclaw, hermes have already demonstrated how individuals can leverage agents to automate tasks.
However, once we move from single-agent capability to multi-agent collaboration, a more fundamental question emerges:
Collaboration is, at its core, a problem of communication.
And in enterprises, communication is never free-form. Instead, it is always constrained by organizational structure. This is precisely what Conway’s Law captures: communication paths are shaped by how organizations are structured.
Consider a typical enterprise with departments of procurement, supply chain, R&D, and operations, each forming distinct functional units. Procurement and supply chain, being closely related, tend to share systems and communication channels. In contrast, procurement and R&D, with weaker contextual overlap, often operate through entirely different systems.
As a result, system architecture is fundamentally a projection of communication pathways, and organizational boundaries naturally become system boundaries. At a global level, inconsistencies are rarely caused by technical limitations, they are more often the result of communication costs. This becomes even more evident in the context of widespread agent adoption.
Communication Paths
The capability of modern AI agents is not only determined by model capacity, but also by the quantity and relevance of their context. When two teams share highly aligned context, communication becomes efficient, and systems built on top of that alignment tend to be reusable. In such environments, introducing agents is significantly easier—the communication pathways between agents, humans, and systems are already well-defined.
Conversely, when teams lack shared context, deploying effective agent systems becomes difficult. The limitation is not the model itself, but the absence of a coherent communication structure.
Organizational Boundaries
Organizational boundaries manifest concretely through interfaces. For example, a procurement team may adopt a new system to manage inventory data. While this data is theoretically useful to the supply chain team, incompatibilities in data interfaces can prevent seamless collaboration, reducing overall efficiency.
This issue persists in agent-driven systems. While agents can process unstructured data, they still rely on APIs, schemas, and data models to ensure reliable interaction. In other words, organizational boundaries do not disappear, they re-emerge as interface constraints.
Communication Cost
All communication has a cost. For humans, this cost appears as meetings, fragmented documentation, and inconsistent understanding. For agents, communication is compressed into tokens. It transforms into computation and uncertainty. Ideally, we want minimal tokens to yield precise and reliable outputs, enabling consistent propagation across systems. In reality, however, the probabilistic nature of large language models makes perfect accuracy unattainable.
Thus, reducing communication cost is not about eliminating uncertainty, but about constraining it. This is precisely what modern harnessing techniques and control frameworks aim to achieve.
What can we do
Returning to the example above, where the enterprise has multiple functions, i.e., procurement, supply chain, operations, and R&D. Assuming each deploys agents to automate their workflows. A Conway-compliant architecture would resemble the diagram shown below. In this setup, each function operates through a combination of human workers and agents; each agent maintains its own local memory to preserve execution history, allowing it to remain coherent when receiving new context and instructions. Agents communicate with those in other functions through standardized interfaces, and during this process, shared context provides both the communication pathways and the boundary conditions that enable efficient collaboration.
As discussed earlier, shared context is the key enabler of smooth communication. However, in designing such systems, this shared context should not be expanded across the entire enterprise. Doing so would lead to “context explosion”, significantly increasing communication costs between agents—effectively a form of inverse Conway’s Law. Instead, shared context should be selectively established among functions with strong overlap and commonality, enabling efficient communication where it is actually needed.
Wrap-up
For organizations seeking to effectively adopt AI agents, understanding Conway’s Law is essential. Within a fixed organizational structure, success depends on:
- Defining clear context to shape communication paths.
- Establishing stable interfaces to enforce boundaries.
- Applying constraints to control communication cost.
Perhaps, we are entering a new era where systems mirror organizational structure is fading, and an era where systems reflect communication structure is beginning.
References
- Conway, M. (1968). How Do Committees Invent?
- Sussman, G. and Steele, G. (1975). Constraints and Communication in System Design.
- Brooks, F. (1975). The Mythical Man-Month
Citation
Plain citation as
Zhang, Le. Conway’s Law in the Age of AI Agent Systems. Thinkloud. https://yueguoguo.github.io/2026/04/01/conway-principle/, 2026
or Bibliography-like citation
@article{yueguoguo2026conwayprinciple,
title = "Conway's Law in the Age of AI Agent Systems",
author = "Zhang, Le",
journal = "yueguoguo.github.io",
year = "2026",
month = "Apr",
url = "https://yueguoguo.github.io/2026/04/01/conway-principle/"
}