With the rapid rise of AI-powered automation, building a “Plan and Execute” AI agent in n8n has become one of the most practical ways to enhance workflows using generative AI. Whether you're an entrepreneur looking to streamline research tasks or an operations manager automating strategy execution, a n8n plan and execute agent can help you build task-aware, multi-step logic that reasons before acting. In this guide, we’ll walk you through how to create one from scratch using n8n's powerful workflow engine—no DevOps team required.
What is a Plan and Execute Agent?
A Plan and Execute agent is a system that splits its task into two parts:
- Plan: Given a user input or request, the agent breaks down the task into a step-by-step actionable plan.
- Execute: The agent then performs each step in the plan sequentially or in parallel, leveraging tools like APIs, functions, or AI models.
This agent style is particularly effective in scenarios like document summarization, competitive research, or multi-step business workflows where a single prompt isn't enough.
Unlike monolithic LLM calls, the Plan and Execute methodology introduces reasoning and reduces hallucination errors.
Why Build It in n8n?
n8n is an open-source, node-based workflow automation engine that offers:
- Visual no-code interface
- Built-in LLM integration (OpenAI, Ollama, etc.)
- Conditional logic and loops
- Easy error handling and monitoring
Using n8n allows you to rapidly prototype, customize, and deploy agents without relying on expensive SaaS tools or writing thousands of lines of backend code.
What You’ll Need Before Starting
- A running n8n instance (you can install it for free if you don't have one)
- An OpenAI API key (or access to a local model via Ollama)
- Basic familiarity with n8n nodes and workflow execution
You can optionally extend this with custom nodes or community packages, like we covered in how to install custom nodes in n8n.
Step-by-Step: Build a "n8n Plan and Execute Agent"
Step 1: Capture the User Input
Start with a Webhook or Manual Trigger node to capture the user query. This might look like:
"Summarize the latest updates from OpenAI’s blog and generate key takeaways."
Step 2: Generate a Task Plan
Add an OpenAI node and configure it to act as a planner.
Prompt Example:
You are a task planner. Break down the following request into clear, logical steps:
{{ $json["query"] }}
Provide the plan in JSON format:
[
{"step": "Search for the latest OpenAI blog updates"},
{"step": "Summarize each post"},
{"step": "List the key takeaways"}
]
Set the response mode to JSON to keep output parsing clean.
Step 3: Parse the Plan
Use a Set or Code node to map each plan item into a structured loop. Here’s where n8n shines.
Example with Code Node:
const plan = $json["choices"][0]["message"]["content"];
return JSON.parse(plan).map((item, index) => ({
json: {
step_number: index + 1,
task: item.step
}
}));
This outputs each step as an individual item in the loop.
Step 4: Loop Through and Execute Each Step
Add a SplitInBatches node to process one step at a time. Then, for each:
- Use a conditional
Switchnode to determine how to handle the step. - Add sub-workflows (like a scraper, summarizer, or database writer) accordingly.
For AI-based execution, you could use another OpenAI node or make a call to LangChain if needed.
Visual Tip:
A helpful visual representation would be a flowchart table:
| Step | Action | Tool Used |
|---|---|---|
| 1 | Generate Plan | OpenAI |
| 2 | Parse JSON into steps | Code node |
| 3 | Loop through each step | SplitInBatches |
| 4 | Execute per task | Switch & AI nodes |
Add a NoOp node at the end to signal success or feeding into another output trigger.
Real Use Case Example
Imagine building a Competitive Research Agent. A user inputs:
"Give me a summary of Apple and Samsung's product releases this month."
- The agent plans:
- Search press releases for both companies
- Extract product-related news
- Summarize findings
- Each step is executed using scraper APIs + OpenAI summarization
- Agent compiles a clean multi-paragraph report with bullet points
This gives the user accurate, task-partitioned output vs. a single fuzzy LLM response.
Want to expand your AI agent capabilities? You might want to also explore how to build a conversational agent using n8n.
Tips to Scale Your n8n Plan and Execute Agent
- Cache planning steps: Avoid redundant planning with Redis or a local key-value store.
- Add error retries using n8n’s built-in error handling tactics.
- Use local models via Ollama to cut costs and avoid rate limits.
Also, for more flexibility, explore using community packages in n8n to integrate Python functions, headless browsers, or scheduling logic.
FAQ
What makes an "n8n plan and execute agent" different from a basic LLM call?
A plan and execute agent first breaks down a user query into logical tasks, which are then completed sequentially. This approach builds reliability, traceability, and reduces hallucination—unlike a one-shot LLM response.
Can I use local models instead of OpenAI?
Yes. n8n supports integration with local models like Ollama for running LLMs offline, which is useful for privacy or cost-sensitive projects.
Is this suitable for production use?
Absolutely—with proper error handling, rate limiting, and logging, n8n-based plan and execute agents can be production-grade, especially when self-hosted.
How advanced can I make this agent?
Very. You can loop in document parsing, PDF generation, Excel exports, multi-threaded execution, user authentication layers, etc. It can be as simple or complex as your use case demands.
Do I need to know how to code?
Basic JavaScript helps, especially in Code nodes, but 90% of the workflow can be built visually using n8n’s no-code interface.
By combining the power of structured planning with execution tools inside n8n, you can easily build AI agents that perform robust, broken-down, and task-aware automation routines—perfect for LLM workflows, knowledge management, and more. Whether you're just getting started or scaling up your automation strategy, this method levels up both productivity and reliability.