Architecture
An assembly-line architecture where agents are arranged in a strict sequence. Each agent transforms or enriches the output of the previous one, then passes it forward. Best for tasks with clear sequential dependencies — document processing, content production pipelines, compliance workflows.
Trigger { # User
n1: circle label="Start"
n2: rectangle label="Submit complex task"
n1.handle(right) -> n2.handle(left)
n2.handle(bottom) -> Pipeline.n3.handle(top) [label="Task"]
}
Pipeline { # Sequential Pipeline
n3: rectangle label="Agent A: Parse input"
n4: rectangle label="Agent B: Enrich data"
n5: rectangle label="Agent C: Analyze"
n6: rectangle label="Agent D: Format output"
n7: circle label="Done"
n3.handle(right) -> n4.handle(left)
n4.handle(right) -> n5.handle(left)
n5.handle(right) -> n6.handle(left)
n6.handle(right) -> n7.handle(left)
n6.handle(top) -> Trigger.n2.handle(bottom) [label="Final output"]
}
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 sequential pipeline multi-agent architecture where agents are arranged in a strict sequence. Each agent transforms or enriches the output of the previous one, then passes it forward. No central orchestrator — the flow is deterministic like an assembly line.
Architecture
The simplest AI-native architecture — a single agent that receives user input, reasons, plans, decides on tool calls, processes results, and generates responses. Direct MCP connection over stdio or HTTP. Best for MVPs and when low latency matters.
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.