Architecture
An org-chart architecture with multi-level structure. A top-level supervisor manages team leads, who each manage their own pool of specialist workers. Teams within teams. Best for enterprise-scale automation with 10+ specialized agents spanning multiple domains.
User { # User
n1: circle label="Start"
n2: rectangle label="Submit enterprise request"
n1.handle(right) -> n2.handle(left)
n2.handle(bottom) -> Supervisor.n3.handle(top) [label="Goal"]
}
Supervisor { # Top-Level Supervisor
n3: rectangle label="Decompose goal"
n4: rectangle label="Delegate to team leads"
n5: rectangle label="Aggregate final output"
n6: circle label="Done"
n3.handle(right) -> n4.handle(left)
n4.handle(bottom) -> LeadA.n7.handle(top) [label="Sub-goal A"]
n4.handle(bottom) -> LeadB.n10.handle(top) [label="Sub-goal B"]
n5.handle(right) -> n6.handle(left)
n5.handle(top) -> User.n2.handle(bottom) [label="Final report"]
}
LeadA { # Team Lead A
n7: rectangle label="Plan subtasks for A"
n8: rectangle label="Dispatch to workers"
n9: rectangle label="Consolidate A results"
n7.handle(right) -> n8.handle(left)
n8.handle(bottom) -> Workers.n13.handle(top) [label="Task A1"]
n8.handle(bottom) -> Workers.n14.handle(top) [label="Task A2"]
n9.handle(top) -> Supervisor.n5.handle(bottom) [label="Result A"]
}
LeadB { # Team Lead B
n10: rectangle label="Plan subtasks for B"
n11: rectangle label="Dispatch to workers"
n12: rectangle label="Consolidate B results"
n10.handle(right) -> n11.handle(left)
n11.handle(bottom) -> Workers.n15.handle(top) [label="Task B1"]
n11.handle(bottom) -> Workers.n16.handle(top) [label="Task B2"]
n12.handle(top) -> Supervisor.n5.handle(bottom) [label="Result B"]
}
Workers { # Worker Agents
n13: rectangle label="Worker A1 executes"
n14: rectangle label="Worker A2 executes"
n15: rectangle label="Worker B1 executes"
n16: rectangle label="Worker B2 executes"
n13.handle(top) -> LeadA.n9.handle(bottom) [label="A1 done"]
n14.handle(top) -> LeadA.n9.handle(bottom) [label="A2 done"]
n15.handle(top) -> LeadB.n12.handle(bottom) [label="B1 done"]
n16.handle(top) -> LeadB.n12.handle(bottom) [label="B2 done"]
}
Architecture
A hierarchical multi-agent architecture that scales orchestration by stacking supervisors and team leads (a tree structure), which mirrors enterprise org structures and helps partition context. This is the 'enterprise-grade agentic AI architecture' when a single orchestrator cannot manage all workers directly. Ideal for large enterprises and multi-domain workflows.
Architecture
A hierarchical multi-agent architecture with multi-level organizational structure. A top-level executive agent manages mid-level team lead agents, who each manage their own pool of specialist workers. Teams within teams — the hierarchy maps to domain separation.
Architecture
A conductor-style architecture where one orchestrator agent receives a complex task, breaks it into subtasks, dispatches each to specialist worker agents (research, code, review), collects results, and synthesizes the final answer. Best for complex multi-step tasks with dynamic decomposition.
Architecture
A map-reduce style architecture where a coordinator fans out tasks to multiple parallel worker agents (style check, security audit, performance analysis), gathers all results, and makes an aggregate decision. Best for PR reviews, code reviews, and multi-dimensional analysis.
Architecture
A tournament-mode architecture where multiple generator agents produce independent outputs in parallel, then an evaluator agent scores and selects the best. Quality threshold checking with refinement loops. Best when correctness or creativity matters more than latency.
Architecture
An orchestrator-worker architecture where an orchestrator agent breaks a goal into subtasks, dispatches to specialized workers, then synthesizes a final response. This is the most common 'agent orchestration' architecture—powerful but the orchestrator can become a bottleneck as the number of workers grows. Frameworks like LangGraph focus on explicit routing/state to manage this.