Multi-agent topologies

Four shapes describe how multiple agents wire together: the hub that delegates, the hierarchy that decomposes, the mesh that talks peer to peer, the swarm that explores in parallel. Hub-and-spoke dominates production in 2026; mesh and swarm rarely survive contact with debugging and governance.

Each shape is a bet on a failure mode. Click a topology for what it is good at, and what breaks it in production.

The four shapes

The hub-and-spoke pattern is one in which a central agent plans and delegates to a group of specialized workers. All coordination flows through the hub and the workers do not talk to each other. This is a dominant approach in 2026 when it comes to production deployments. It is a very effective pattern for tightly scoped, sequential problems as well as compliance checks and financial analysis. The one drawback is that the supervisor agent becomes a bottleneck in some cases, when tasks become exploratory and its context window fills up.

The hierarchical topology is another pattern, where we see a tree structure comprising manager, specialist and worker tiers. The manager agent delegates to specialists who delegate further to the worker tiers. This approach is utilized for complex enterprise workflows requiring multi-level domain expertise and clear decomposition.

Flat mesh (peer-to-peer) is an approach in which agents exchange information directly without central control. This is a high fault tolerance pattern that is also very effective for dynamic discovery tasks. However, it risks drift and fragmentation without aggregation, and that is why it is rarely seen in production: it is extremely difficult to debug and govern.

Swarm is the last of the topologies and it describes parallel agents exploring independently with redundancy validating signals. It is an approach good for research and creative tasks. The risk is elevated token usage if strict exit conditions are not present. Currently it is mostly an academic pattern.

Earn your complexity

Three fundamentals frame the choice. The first: the architectural choice that matters is not which model to use, but whether we need one agent or many and how they coordinate. The second: complexity must be earned. Start simple and add it only when necessary, even if a multi-agent system sounds more sophisticated. Literature is full of complex swarm architectures while production is dominated by hub-and-spoke approaches. The third: splitting reasoning across agents only pays off if the messages passed between them keep the information the task actually needs. Controlled experiments across five benchmarks and three model scales found multi-agent decomposition helps most with weaker models; stronger models, which already pull useful signal out of one long shared context, gain little from splitting up the work and can do worse when the messages between agents drop information the task needed.