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.
Input { # Input
n1: circle label="Start"
n2: rectangle label="Creative brief submitted"
n1.handle(right) -> n2.handle(left)
n2.handle(bottom) -> Generators.n3.handle(top) [label="Brief"]
n2.handle(bottom) -> Generators.n4.handle(top) [label="Brief"]
n2.handle(bottom) -> Generators.n5.handle(top) [label="Brief"]
}
Generators { # Generator Agents
n3: rectangle label="Agent A: Draft option 1"
n4: rectangle label="Agent B: Draft option 2"
n5: rectangle label="Agent C: Draft option 3"
n3.handle(bottom) -> Evaluator.n6.handle(top) [label="Option 1"]
n4.handle(bottom) -> Evaluator.n6.handle(top) [label="Option 2"]
n5.handle(bottom) -> Evaluator.n6.handle(top) [label="Option 3"]
}
Evaluator { # Evaluator Agent
n6: rectangle label="Score all options"
n7: diamond label="Quality threshold met?"
n8: rectangle label="Select best output"
n9: circle label="Done"
n6.handle(right) -> n7.handle(left)
n7.handle(right) -> n8.handle(left) [label="Yes"]
n7.handle(top) -> Generators.n3.handle(bottom) [label="No - Refine"]
n8.handle(right) -> n9.handle(left)
loop [refine until quality threshold met] n3 n4 n5 n6 n7
}
Architecture
A competitive / generator-critic architecture where multiple generators produce independent answers, then an evaluator agent scores and selects the best output. This approach improves quality and reduces single-model brittleness. It's costlier (multiple LLM calls) but pays off when correctness or creativity matters more than latency.
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
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.
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
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.
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.