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.
User { # User
n1: circle label:"Start"
n2: rectangle label:"Send request"
n1.handle(right) -> n2.handle(left)
n2.handle(bottom) -> Agent.n3.handle(top) [label="Request"]
}
Agent { # Single AI Agent
n3: rectangle label:"Receive input"
n4: rectangle label:"Reason and plan"
n5: rectangle label:"Decide tool call"
n6: rectangle label:"Process tool result"
n7: rectangle label:"Generate response"
n8: circle label:"Done"
n3.handle(right) -> n4.handle(left)
n4.handle(right) -> n5.handle(left)
n5.handle(bottom) -> Tools.n9.handle(top) [label="MCP request"]
n6.handle(right) -> n7.handle(left)
n7.handle(right) -> n8.handle(left)
n7.handle(top) -> User.n2.handle(bottom) [label="Response"]
loop [retry until goal met] n4 n5 n6 n7
}
Tools { # Tool Server (MCP)
n9: rectangle label:"Receive MCP call"
n10: rectangle label:"Execute tool"
n11: rectangle label:"Return result"
n9.handle(right) -> n10.handle(left)
n10.handle(right) -> n11.handle(left)
n11.handle(top) -> Agent.n6.handle(bottom) [label="Tool result"]
}
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 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
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 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.