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20 templates
End-to-end agentic commerce workflow showing how an AI assistant (ChatGPT) orchestrates product search, checkout, and payment through Shopify's Universal Commerce Protocol (UCP) gateway, enabling conversational shopping experiences.
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.
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.
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.
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.
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.
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.
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.
A supervisor-worker multi-agent architecture where an orchestrator agent receives a high-level goal, breaks it into subtasks, delegates each to specialist worker agents (researcher, writer, QA), monitors execution, handles failures, and synthesizes results. The orchestrator manages but does not execute tasks itself.
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.
A swarm multi-agent architecture where multiple agents work on the same or related tasks simultaneously, with no central coordinator. Their outputs are then aggregated, voted on, or merged by a dedicated aggregator node. Agents may compete — the best output wins.
A hierarchical multi-agent architecture with multi-level organizational structure. A top-level executive agent manages mid-level team lead agents, who each manage their own pool of specialist workers. Teams within teams — the hierarchy maps to domain separation.
The most architecturally sophisticated multi-agent pattern. Agents are assigned not just roles but opposing incentives. A Planner is optimistic about goal completion. A Critic is constitutionally skeptical and holds veto authority. Errors get caught through adversarial pressure rather than trusting a single model's self-assessment.
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.
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.
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.
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.
A multi-agent mesh pattern where agents communicate through a shared context broker backed by MCP. Enables coordinated tool access and state synchronization across multiple specialized agents (planner, coder, reviewer, operator). Supports both orchestrated and choreographed interaction patterns.
Multi-agent coordination pattern where an orchestrator breaks work into subtasks, specialist agents pull from and push to a shared state store, and the orchestrator composes the final answer from that shared state. Multi-agent setups feel coherent instead of each assistant having its own inconsistent memory.
Multi-agent security architecture with scoped tokens, API gateway enforcement, mTLS-protected worker calls, and orchestrator-driven result aggregation.