Hierarchical (Supervisor) Topology
Organise multi-agent systems as hierarchies — supervisors delegate to sub-supervisors or specialists — mirroring effective human organisational structures.
This page is the complete instruction page for one pattern called "Hierarchical (Supervisor) Topology." It explains the problem this pattern solves, the idea behind the solution, when you should (and should not) use it, and what happens afterward — both the good effects and the costs.
This matters because building AI agents is not just about making them clever. It is also about making them safe and predictable. Following a well-tested pattern like this one helps avoid common mistakes, and shows you exactly which safety rules and regulations it connects to, listed under "Standards Mesh" on this page.
Context
Flat agent pools do not scale; every agent has to know about every other. Hierarchies localise decisions and bound communication.
Problem
Peer-to-peer coordination scales quadratically; hierarchy scales linearly.
Forces
- Clear chain of responsibility vs rigidity
- Local decisions vs global optimisation
Solution
Top-level supervisor owns the overall goal. It delegates to domain supervisors, which delegate to specialists. Results propagate upward. Escalation is a first-class path: a specialist can raise to its supervisor when out of scope.
Applicability
- Enterprise-scale multi-agent systems
- Customer service with specialisation
- Operations orchestration
Anti-Patterns
- Hierarchy with no escalation path
- Supervisors without real authority over workers
Consequences
- +Clear responsibility
- +Scales organisationally
- −Rigid; can miss cross-branch collaboration
- −Supervisor single-point-of-failure