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.
User { # User
n1: circle label:"Start"
n2: rectangle label:"Submit complex task"
n1.handle(right) -> n2.handle(left)
n2.handle(bottom) -> Orchestrator.n3.handle(top) [label="Task"]
}
Orchestrator { # Orchestrator Agent
n3: rectangle label:"Receive task"
n4: rectangle label:"Break into subtasks"
n5: rectangle label:"Dispatch subtasks"
n6: rectangle label:"Collect results"
n7: rectangle label:"Synthesize final answer"
n8: circle label:"Done"
n3.handle(right) -> n4.handle(left)
n4.handle(right) -> n5.handle(left)
n5.handle(bottom) -> Research.n9.handle(top) [label="Research subtask"]
n5.handle(bottom) -> Code.n11.handle(top) [label="Code subtask"]
n5.handle(bottom) -> Review.n13.handle(top) [label="Review subtask"]
n6.handle(right) -> n7.handle(left)
n7.handle(right) -> n8.handle(left)
n7.handle(top) -> User.n2.handle(bottom) [label="Response"]
}
Research { # Research Agent
n9: rectangle label:"Search sources"
n10: rectangle label:"Summarize findings"
n9.handle(right) -> n10.handle(left)
n10.handle(top) -> Orchestrator.n6.handle(bottom) [label="Research result"]
}
Code { # Code Agent
n11: rectangle label:"Generate code"
n12: rectangle label:"Run tests"
n11.handle(right) -> n12.handle(left)
n12.handle(top) -> Orchestrator.n6.handle(bottom) [label="Code result"]
}
Review { # Review Agent
n13: rectangle label:"Evaluate quality"
n14: rectangle label:"Flag issues"
n13.handle(right) -> n14.handle(left)
n14.handle(top) -> Orchestrator.n6.handle(bottom) [label="Review result"]
}
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 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 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.
Architecture
An event-driven agentic AI architecture that replaces the central orchestrator with Kafka/PubSub topics: agents subscribe, react, and publish new events. This aligns multi-agent systems with proven microservices choreography and is ideal for real-time, high-throughput systems and 'agent mesh' setups.