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.
Trigger { # Trigger
n1: circle label:"Start"
n2: rectangle label:"PR submitted for review"
n1.handle(right) -> n2.handle(left)
n2.handle(bottom) -> Coordinator.n3.handle(top) [label="PR payload"]
}
Coordinator { # Coordinator Agent
n3: rectangle label:"Fan out to reviewers"
n4: rectangle label:"Gather all reviews"
n5: diamond label:"All passed?"
n6: rectangle label:"Approve PR"
n7: rectangle label:"Request changes"
n8: circle label:"Done"
n3.handle(bottom) -> Reviewers.n9.handle(top) [label="Style check"]
n3.handle(bottom) -> Reviewers.n10.handle(top) [label="Security audit"]
n3.handle(bottom) -> Reviewers.n11.handle(top) [label="Perf analysis"]
n4.handle(right) -> n5.handle(left)
n5.handle(right) -> n6.handle(left) [label="Yes"]
n5.handle(bottom) -> n7.handle(top) [label="No"]
n6.handle(right) -> n8.handle(left)
}
Reviewers { # Parallel Review Agents
n9: rectangle label:"Style Agent"
n10: rectangle label:"Security Agent"
n11: rectangle label:"Performance Agent"
n9.handle(top) -> Coordinator.n4.handle(bottom) [label="Style report"]
n10.handle(top) -> Coordinator.n4.handle(bottom) [label="Security report"]
n11.handle(top) -> Coordinator.n4.handle(bottom) [label="Perf report"]
}
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
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
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
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.