Planner-Executor Pattern
Splitting agents into a planner LLM and a smaller executor LLM.
/ quick answer
A large model plans the steps and delegates each step to a small, fast, cheap model. Cuts cost while keeping strategy quality — the workhorse pattern for production agents. Splitting agents into a planner LLM and a smaller executor LLM.
/ frequently asked
What is Planner-Executor Pattern?
A large model plans the steps and delegates each step to a small, fast, cheap model. Cuts cost while keeping strategy quality — the workhorse pattern for production agents.
What is an example of Planner-Executor Pattern?
Claude Sonnet plans; Haiku executes each tool call.
Why does Planner-Executor Pattern matter?
Splitting agents into a planner LLM and a smaller executor LLM.
What should I learn after Planner-Executor Pattern?
Open the related workflows and prompts further down this page: they show how this idea is applied in a real task, step by step.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →MCP (Model Context Protocol)
Open protocol that lets LLMs connect to tools, data sources and apps through a standard interface.
- →Agentic RAG
RAG where an agent decides what to retrieve, when, and from which source — instead of a single static query.
- →Agentic Workflow
A workflow where an LLM decides the next step instead of a hard-coded path.
- →Tool Use
An LLM invoking external functions to act beyond text generation.
Related workflows
Turn this into a repeatable process.
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- →How to Build an AI Content System
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- →Personal Research Assistant Workflow
A repeatable system to research any topic deeply in under 30 minutes.
Related tool stacks
The tools that run it in production.
- →Agent Research Stack
Web-search-enabled agent for autonomous research tasks.
- →AI Research & Knowledge Stack
Default toolset for analysts, founders and creators doing deep research with AI.
Comparisons & alternatives
Pick between the options.
- →OpenAI vs Anthropic for Agents
Which provider builds better production agents in 2026.
- →CrewAI vs AutoGen vs LangGraph
Three approaches to multi-agent orchestration.
- →Cursor Agents vs Devin vs Lovable
Three ways AI writes code for you in 2026.
- →OpenAI vs Gemini for Agent Building
Both ship strong models and SDKs — differences are in tool calling, context and pricing.