Create Your First AI Planning Agent in 10 Minutes (n8n Tutorial)

Let's cut through the noise. You don't need complex architectures or months of development to build useful AI agents. What you need is a clear problem, simple tools, and a workflow that actually works.

I built this planning agent to solve my own decision fatigue. When I'm staring at a blank page trying to plan a project, I need structure, not another chat session. This agent gives me that in seconds.

What You're Building Today

You're creating an AI agent with three core functions:

1. Accepts any task description (via webhook or manual input)
2. Analyzes and structures the task into logical steps
3. Outputs prioritized next actions with time estimates

The beauty is in the simplicity. No vector databases, no complex memory systems—just a clean input → process → output flow that delivers immediate value.

Setting Up Your n8n Environment

First, make sure you have n8n running. You can use:

• n8n.cloud (easiest, free tier available)
• Self-hosted (Docker or npm install)
• Desktop app (for local testing)

I recommend starting with n8n.cloud for simplicity. Create an account, and you're ready in minutes.

The Complete Workflow Structure

Here's the node-by-node breakdown of what we're building:

1. Webhook Node (Trigger)
   - Receives task input
   - Can be triggered manually or via API

2. OpenAI Node (Planning Engine)
   - Contains the planning prompt
   - Processes the task
   - Returns structured JSON

3. Function Node (Response Parser)
   - Extracts and validates JSON
   - Formats for output
   - Handles errors gracefully

4. Output Node (Results)
   - Displays the structured plan
   - Can connect to other tools

The Planning Prompt That Actually Works

After testing dozens of variations, this prompt consistently delivers usable plans:

ROLE: Expert Planning Assistant

TASK: Analyze the following task and create an actionable plan.



INPUT: {{ $json.body.task }}



OUTPUT FORMAT: Return ONLY valid JSON with this structure:

{

  "analysis": {

    "complexity": "simple/medium/complex",

    "domain": "primary category",

    "key_challenges": ["list of 2-3 main challenges"]

  },

  "plan": {

    "total_steps": number,

    "estimated_total_time": "X hours",

    "steps": [

      {

        "id": 1,

        "title": "Clear action title",

        "description": "Specific what and why",

        "priority": 1-3,

        "time_estimate": "minutes/hours",

        "depends_on": ["step IDs"],

        "resources": ["tools/people/info needed"]

      }

    ]

  },

  "next_actions": [

    {

      "action": "Immediate next step",

      "why_first": "Reason for priority",

      "time_to_complete": "X minutes",

      "blockers": ["potential obstacles"]

    }

  ]

}



RULES:

- Break tasks into 3-7 manageable steps

- Each step must start with an action verb

- Include realistic time estimates

- Identify dependencies between steps

- List 2-3 next actions maximum

- Be practical, not theoretical

This prompt works because it's specific about format, includes domain analysis, and focuses on actionable outputs.

Configuring the OpenAI Node

In your n8n workflow:

1. Add an "OpenAI" node
2. Connect it to your Webhook node
3. Set model to "gpt-3.5-turbo" (cheaper and fast enough for planning)
4. Temperature: 0.3 (we want consistent, structured output)
5. Max Tokens: 1500 (enough for detailed plans)
6. Paste the prompt above into the "Prompt" field

Connect the task input like this:

// In the OpenAI node settings:
Model: gpt-3.5-turbo
Temperature: 0.3
Max Tokens: 1500
System Message: (leave empty or use minimal guidance)
User Message: {{$json.prompt}} // Your full prompt with {{$json.task}} variable

Handling the AI Response

The AI will return text that includes JSON. We need to extract and validate it:

// Function node: Parse AI Response
// Clean the AI response to remove markdown formatting if present
let rawContent = $input.first().json.text;

// Remove markdown backticks and 'json' label
const cleanJson = rawContent.replace(/```json|```/g, "").trim();

try {
  const data = JSON.parse(cleanJson);
  return {
    json: data
  };
} catch (e) {
  // If parsing still fails, return the raw content so you can debug
  return {
    json: {
      error: "JSON Parsing failed",
      raw: cleanJson
    }
  };
}

Testing with Real Examples

Don't test with "hello world" tasks. Use real scenarios you actually face:

Example 1: Content Creation

Task: "Create a tutorial video about setting up n8n webhooks"

Expected output:
- Analysis: medium complexity, video production domain
- Steps: script writing, recording, editing, publishing
- Next actions: research similar tutorials, outline script

Example 2: Project Setup

Task: "Set up a new client project tracking system"

Expected output:
- Analysis: complex, project management domain
- Steps: requirements gathering, tool selection, implementation, training
- Next actions: schedule client discovery call, research tools

Example 3: Learning New Skill

Task: "Learn React fundamentals in 2 weeks"

Expected output:
- Analysis: medium complexity, learning/development
- Steps: find resources, setup environment, build practice projects
- Next actions: choose learning platform, install Node.js

Common Issues and Solutions

Problem: AI ignores JSON structure

Solution: Add "Return ONLY valid JSON" at the beginning AND end of your prompt. Some models need the reminder.

Problem: Steps are too vague

Solution: Add examples in your prompt: "Example step: 'Create outline with 5 main sections' not 'Plan content'"

Problem: Time estimates are unrealistic

Solution: Provide calibration: "For planning purposes, assume: simple task=1-2 hours, medium=3-8 hours, complex=1-3 days"

Problem: No dependencies identified

Solution: Ask explicitly: "Identify which steps must be completed before others can begin"

Enhancing Your Basic Agent

Once you have the core working, consider these additions:

Add Memory with Google Sheets

Store plans to identify patterns and improve future planning:

// After successful planning, append to Google Sheets
const sheetRow = {
  date: new Date().toLocaleDateString(),
  task_type: $json.task.substring(0, 50),
  steps: plan.plan.total_steps,
  estimated_time: plan.plan.estimated_total_time,
  complexity: plan.analysis.complexity
};

// Use n8n's Google Sheets node with:
// Operation: Append
// Sheet: Planning History
// Columns match your row structure

Connect to Task Managers

Automatically create tasks from next actions:

// For each next action, create a task
plan.next_actions.forEach((action, index) => {
  const task = {
    name: action.action,
    description: `Priority: ${index + 1}/${
      plan.next_actions.length}\n` +
      `Reason: ${action.why_first}\n` +
      `Time: ${action.time_to_complete}`,
    tags: ['ai-planned', 'automated']
  };
  
  // Send to Todoist, ClickUp, or your task manager
});

Add Quality Scoring

Rate plans based on completeness and specificity:

function scorePlan(plan) {
  let score = 0;
  
  // Points for specific elements
  if (plan.analysis.key_challenges?.length > 0) score += 20;
  if (plan.plan.steps?.every(s => s.time_estimate)) score += 30;
  if (plan.next_actions?.every(a => a.blockers)) score += 25;
  if (plan.plan.steps?.length >= 3 && plan.plan.steps?.length <= 7) score += 25;
  
  return score;
}

const qualityScore = scorePlan(plan);
const quality = qualityScore >= 80 ? 'high' : 
                qualityScore >= 60 ? 'medium' : 'low';

Why This Approach Wins

I've tried both complex agent frameworks and simple chat interfaces. Here's what I've learned:

Complex frameworks require weeks of setup, constant maintenance, and often break with API changes. You spend more time fixing the system than using it.

Chat interfaces give you inconsistent output. Same task, different day, completely different structure. You waste time reformatting and extracting actions.

This n8n agent gives you consistency with flexibility. The structure is fixed, but the planning intelligence adapts to each task. It's maintainable, debuggable, and actually gets used.

I've been running this exact workflow for 3 months. It's planned 47 content pieces, 12 client projects, and countless personal tasks. The time savings? About 2-3 hours per week of mental planning overhead.

FAQ

Q: Can I use a free AI model instead of OpenAI?

A: Yes. Replace the OpenAI node with DeepSeek, Google Gemini, or any model that supports structured output. The prompt engineering principles remain the same.

Q: How do I handle very large tasks that exceed token limits?

A: Break them down. First, ask the agent to create a high-level breakdown. Then, feed each major component through the agent separately. Chain the workflows.

Q: What if I need domain-specific planning?

A: Modify the system prompt. Add: "You are an expert [your domain] planner with 10 years of experience." Include domain-specific considerations in the rules section.

Q: Can this run automatically on a schedule?

A: Absolutely. Replace the Webhook trigger with a Schedule trigger. Have it read tasks from a Google Sheet or database and process them daily.

Q: How do I share this with my team?

A: Deploy your n8n workflow, then share the webhook URL. Team members can submit tasks via a simple form (use n8n's Form Trigger node) or API.

Output

Next Steps and Template

You now have a functional AI planning agent. The real work begins when you start using it daily and iterating based on what you need.

Maybe you need better integration with your existing tools. Maybe you want historical analysis of your planning patterns. Maybe you need multi-agent collaboration where one agent plans and another executes.

Start simple. Use the basic version for a week. Notice where it helps and where it falls short. Then add one enhancement at a time.

I've packaged everything we covered—the complete n8n workflow JSON, tested prompts, error handling code, and setup instructions—into a ready-to-use template. It's the exact configuration I'm running, plus some bonus nodes I've found useful.

Want to skip the setup and get straight to using it? Comment "template" and I'll share the starter kit and workflow directly. No email gates, no courses—just the working files.

or

Everything lives in one place: https://github.com/avnishyadav25/30-day-ai-ship-challenge.

What's the first task you'll run through your new planning agent? A project you've been putting off? A learning goal? I'm curious what you'll build with this foundation.

Thanks
Avnish