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GenAI Architecture Patterns

A deep dive into single-agent, multi-agent, and automation patterns — with trade-offs, decision frameworks, and code examples.

Why Patterns Matter

Choosing the wrong architecture pattern is one of the most common causes of GenAI project failure. Over-engineering a simple task wastes resources; under-engineering a complex one produces brittle, unreliable systems. This guide maps each pattern to the right conditions.

Rule of thumb: Use the simplest pattern that solves the problem. Add agents only when single-agent complexity becomes unmanageable.

Pattern 1 — Single Agent / Deterministic Chain

A single LLM call or a linear sequence of deterministic steps (data fetch → LLM → output). Ideal for predictable, well-scoped tasks with structured inputs.

When to Use

Structured, predictable inputs
Simple Q&A or document summarization
Low-risk, deterministic tasks
Fast prototyping and PoC

Limitations

Cannot handle ambiguous or creative tasks
Context window fills quickly
No self-correction or validation
Poor scalability for complex workflows

Pattern 2 — Multi-Agent Systems

Multiple specialized agents collaborate, each owning a specific responsibility. Four key sub-patterns exist, each suited to different problem types.

Orchestrator-Worker

A central orchestrator decomposes the goal and delegates subtasks to specialized worker agents. Best for complex tasks with clear sub-problems.

Orchestrator → [Worker: DB Query] → [Worker: Summarizer] → [Worker: Formatter] → Response

Router

A router classifies intent and dispatches to the best-suited specialist agent. Best for multi-domain applications where each domain has distinct tooling.

Input → Router (intent detection) → [Sales Agent | Support Agent | HR Agent]

Hierarchical

Supervisor agents verify and approve results from subordinate agents before passing downstream. Best for high-risk workflows requiring quality gates.

Worker → Supervisor (validates) → Worker → Supervisor → Final Output

Critic-Refiner

A generator agent produces a response; a critic evaluates it; a refiner agent improves it. Best for creative or high-quality output tasks.

Generator → Critic (score + feedback) → Refiner → [repeat if needed] → Final

Pattern 3 — Automation Flows (No Agent)

Platforms like n8n or AWS Step Functions orchestrate API calls and data transformations without LLM reasoning. Ideal for deterministic, low-complexity workflows.

Key Point: Not every automation needs an LLM. If the workflow is fully deterministic — fixed inputs, fixed transformations, fixed outputs — skip the agent and use a workflow engine. It's faster, cheaper, and more reliable.

Pattern Comparison

Pattern	Flexibility	Complexity	Cost	Best For
Deterministic Chain	Low	Low	Lowest	Structured, predictable tasks
Single Agent + RAG	Medium	Low–Medium	Low	Q&A, assistants, summaries
Orchestrator-Worker	High	High	High	Complex multi-step tasks
Router	High	Medium	Medium	Multi-domain systems
Critic-Refiner	High	Medium–High	High	Quality-sensitive outputs

Decision Framework

Warning: Jumping straight to multi-agent for a problem that a single agent handles well is a common over-engineering mistake. Validate with the simplest solution first.

Is the task fully deterministic? → Use automation flow (n8n, Step Functions), no LLM needed.
Does it need reasoning but fits one context window? → Use single agent with tools.
Does it require multiple domains or specialists? → Use Router or Orchestrator-Worker.
Does output quality need iterative improvement? → Use Critic-Refiner.
Does it need compliance and quality gates? → Use Hierarchical with supervisor agents.

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