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Gartner LLM-based AI Design Patterns

Overview

Emerging Patterns for Building LLM-Based AI Agents

Patterns for Building LLM-Based Agents

Gartner's diagram outlines patterns across three categories: Agent Architectures, Functional Patterns, and Operational Patterns for LLM-based AI agents.

Agent Architectures

  • Solo Agent: Atomic, monolithic agents for simple tasks.
  • Agent Roles: Define personas to guide behavior and scope.
  • Agent-to-Agent Handoff: Delegation in multi-agent systems.
  • Multi-Agent Modularity: Decomposes tasks across specialized sub-agents.

Functional Patterns

  • Prescribed Plan: Fixed workflows for repeatable processes.
  • Dynamic Plan: Runtime strategy generation for adaptability.
  • MHQA: Multi-hop question answering with iterative retrieval.
  • Collaborating Agents: Dynamic teamwork.
  • Orchestrated Agents: Predefined graphs for controlled interactions.
  • LLM Interaction Patterns: ReAct (reason-act loops), Reflexion (self-critique), Chain of Thought (step-by-step reasoning).

Operational Patterns

  • Agent Evaluation: User-in-the-loop oversight; LLM-as-judge (deterministic).
  • Agent Action Patterns: Function calling, generated code, API tools.
  • Memory Patterns: RAG (retrieval-augmented generation), memory longevity, memory scope.
  • Security Patterns: LLM guardrails, identity tokens, logging.