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.
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
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.
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
A single-agent AI architecture where one agent handles everything: parsing requests, reasoning, calling tools via MCP, and generating responses. This is the default architecture for prototypes and simple automations—easy to debug but hits context-window limits quickly and is hard to parallelize. Ideal for MVPs and solo builders shipping fast.
Architecture
A sequential pipeline architecture chaining multiple agents in a fixed order (parse → enrich → analyze → format), which is a common 'LLM microservices' setup when each step can be isolated. This structure is often used in document processing and ETL-like workflows because each step is testable and predictable.
Architecture
A parallel fan-out architecture that runs multiple agents simultaneously on independent checks (style, security, performance) and then merges results. This is a standard multi-agent design approach for throughput, mapping cleanly to CI/CD, incident response, and research. Fan-in reconciliation becomes the subtle part.