How to build an AI agent that keeps you creative every week (without prompting or thinking)

If you want to ship products but you’re low on ideas, try this: Set up an AI agent that collects real-world signals, extracts user problems, and turns them into high-quality product ideas. You can also do this for content ideas, marketing ideas, you name it.

No brainstorming required. Just a short, ranked list of high-quality ideas automatically delivered every week. Pick one, execute, and repeat.

Here’s exactly how to set that up:

What you’re building

You're building a lightweight AI agent made of three simple parts:

• Inputs - where your agent pulls raw inspiration from
• Processing - how your agent turns that into ideas
• Output - how it formats those ideas into ranked, usable suggestions
  You set this up once. Every week, the agent does its work and you review the ideas. Then, you pick one, ship it, and repeat.
  Let’s get into it, step by step.

Step 1: Define the type of ideas you want

You don’t want “more ideas.” You want the right ideas. So be specific.

Here are a few categories worth focusing on:

• Product innovation – new features, tools, micro-products
• Growth experiments – campaign angles, funnel tests, viral hooks
• Content ideas – blog posts, X threads, educational videos

Choose one of these to start — that way, your agent is laser focused. If you want to add another down the line, you can set up another agent then. We’ll focus on product innovation.

Step 2: Choose your input sources

You’re not feeding the agent manually — you’re pointing it at high-signal places where problems, needs, and patterns show up.

Great input sources:

• Reddit subs like r/SaaS, r/startups, r/Entrepreneur
• Hacker News posts and comment threads
• Indie Hackers posts
• Product Hunt launches
• G2 or Capterra reviews
• Tweets from founders or target users
• Your own customer support tickets, DMs, or emails
  Use RSS.app, Feedly, or Zapier to automatically pull content from 3–5 high-signal sources into a Google Sheet, Notion database, or text folder.

Step 3: Write the agent’s core prompt

This is the instruction set your agent follows each week.
Here’s what it should do:

1. Pull 10–20 new text entries from your sources
2. Extract clear user problems or patterns
3. Generate one idea per problem in your chosen categories
4. Score each idea on originality, impact, and ease
5. Return the top ideas only

Example prompt:

You are an idea-generation assistant for a solo founder.

Every Monday, you’ll receive new posts, reviews, and social content from platforms like Reddit,

Product Hunt, Twitter, and customer feedback channels.
Your job is to extract real problems — then generate buildable, high-leverage product or growth ideas that directly solve those problems.

Here’s what to do:
1. Read through all input text. Scan for repeated complaints, requests, pain points, or recurring questions. Focus on:

• Frustrations about workflows or tools
• Gaps in existing products
• Manual tasks users wish were automated
• High-effort steps people are trying to simplify

2. Select 5 concrete user problems. Each should be:

• Rooted in what users are actually saying
• Specific, not abstract (e.g., “I hate how long this takes” > “Users are unhappy”)
• Actionable — something a solo founder could build around

3. Generate 1 idea for each problem. The idea should:

• Be a product, feature, or tool a solo builder could create
• Be specific and clear (eg, not “AI tool,” but “a browser extension that summarizes customer chats”)
• Directly solve the identified problem
• Avoid generic or overused startup tropes

4. Score each idea on a 1–10 scale in three categories. Then calculate a Total Score (max = 30).

• Originality: Is this a fresh or uncommon solution?
• Impact: Does this solve a meaningful or frequent pain point?
• Speed-to-ship: Could this be built and tested quickly by 1–2 people?

5. Return the top 3–5 highest scoring ideas.

6. Format the output like this:

1. Idea 1

2. Problem: [Brief description]
3. Idea: [1–2 sentence idea]
4. Score: Originality: 8, Impact: 9, Ease: 7 — Total: 24

5. Idea 2

6. ...

Tone and style:

• Be clear, helpful, and straightforward — not clever or verbose
• Use plain language
• Prioritize usefulness over creativity
• Think like a builder solving for momentum

Step 4: Actually build the agent

You don’t need to be technical. You just need to follow these steps and use the right tools. Below are three ways to build your agent — choose the one that fits your style.

Option 1: Zapier + Any AI model
This option is no-code, fully automated, and beginner-friendly.

What you’ll need:

• Zapier (to automate the workflow)
• An AI model (OpenAI, Claude, Gemini, Mistral — any model with an API)
• Google Sheets or Notion for storing inputs and outputs
• RSS.app (or similar) to track content feeds

Step 1: Set up your content feeds using RSS.app or Zapier RSS integration.

• Use RSS.app to create feeds from Reddit, Product Hunt, or other sources. Then connect those feeds to Zapier, and set it to add each new post as a row in your Google Sheet or a new item in your Notion database. That’s how your inputs stay fresh each week.

Step 2: Create a Google Sheet.

• Column A = “Raw Input”
• Column B = “AI Output” (leave blank)

Step 3: Set up your AI model.

• Use Zapier’s “Webhooks by Zapier” action to connect to any AI API — e.g. OpenAI (ChatGPT / GPT-4), Anthropic (Claude), Mistral (open source, via HuggingFace or Replicate), Google Gemini API.
• Paste your prompt into the webhook body.

Step 4: Create your Zap in Zapier:

• Trigger: New row added in your Sheet (or new item in Notion).
• Action 1: Send Column A to your AI model using a webhook + your prompt.
• Action 2: Paste the result into Column B or your Notion database.

Step 5: Schedule it to run weekly:

• In Zapier, add a Schedule trigger for “Every Monday at 9 AM”
• Pull in the latest feed items and run the zap
  Done. You’ve built your first idea agent.

Option 2: Make.com

This option gives more control, combines multiple sources, and offers visual workflows.

What you’ll need:

• A Make account (make.com)
• RSS feeds from RSS.app, Reddit, Product Hunt, etc.
• An AI model with an API (e.g. OpenAI, Claude, Gemini)
• Notion, Google Sheets, or Airtable to store the ideas

Step 1: Create a new Scenario in Make

• Log into Make
• Click “Create a new scenario”

Step 2: Set a Schedule trigger

• Click the clock icon in the top bar
• Choose “Every Sunday at 8PM” (or whatever time works for you)

Step 3: Add an RSS module

• Click “+”, search for “RSS”, and select ‘Watch RSS feed items’
• Paste in your feed URL from RSS.app (e.g. Reddit, Product Hunt, Indie Hackers)
• Set it to pull new items only

Step 4: Add a Text Aggregator module

• Click “+”, search for “Tools > Text Aggregator”.
• Combine all the new items into one big block of text.
• This will become the input for your AI model.

Step 5: Add an HTTP module to call your AI model

• Click “+”, search for “HTTP”, and select ‘Make a request’.
• Method: POST
• Paste in your AI provider’s API endpoint (e.g. OpenAI, Claude, Mistral).
• Add your API key under Headers.
• In the body, send: Your prompt (as the “system” message), and the combined content block (as the “user” message).
  Tip: Most AI providers (like OpenAI or Anthropic) offer copy-paste templates for this in their API docs. You just need to plug in your content and prompt.

6. Add a Notion or Google Sheets module

• Add another module: choose Notion > Create Database Item or Google Sheets > Add Row
• Map the AI response into your destination fields (title, idea, score, etc.).

That’s it. Your agent now runs on autopilot and returns formatted ideas based on real user signals.

Option 3: Manual custom GPT

If you don’t want to automate yet, start by using a Custom GPT inside ChatGPT Pro.

Step 1: Go to chat.openai.com/gpts
Step 2: Click “Create” and name it something like Innovation Agent
Step 3: Under “Instructions”, paste your prompt from earlier
Step 4: Each week, collect 5–10 posts or reviews

• Paste them into the GPT
• Let it return a clean list of ideas
• Copy into Notion or your system of choice
  This isn’t automated — but it is simple, fast, and a great way to validate your workflow before setting up tools. You can scale from there.

Step 5 (optional): Advanced add-ons

Once it’s working, here’s how to make your agent smarter:

• Add scoring memory: Store past outputs and re-score based on actual outcomes (e.g., traffic, engagement)
• Avoid duplicates: Connect to a vector DB (like Pinecone or Weaviate) to track past ideas and avoid repeats
• Auto-prioritize based on your goal: Have your agent auto-rank based on your current goal (growth, low-effort, speed-to-execute)
• Add lightweight tagging: Tag each idea with labels like #quick-win, #technical, or #repurpose to make filtering, planning, and delegation easier over time
• Slack notifications: Send top 3 ideas to yourself (or your team) via Slack every Monday

Step 6: Format the output to fit your workflow

Now, decide how you want to receive and review your ideas.

Here are a few clean formats:

• A Notion board with categories and scores
• A Slack message sent every Monday
• A simple markdown file, like /weekly-ideas/week-17.md
• An email with a short bullet list

Keep the output simple and skimmable. Each idea should include three things:

• The idea
• The problem it solves
• The score to help you prioritize

Here’s what the output might look like:

• Problem: Users drop off after free trials without giving feedback.
• Idea: Trigger a short exit survey with a “roast my product” hook. Turn responses into a weekly Twitter thread.
• Score: 9.1

Step 7: Review weekly. Ship one thing

This isn’t just a way to collect ideas. It’s a system to help you build and launch consistently.
Here’s a simple weekly loop:

• Monday: Review the agent’s output and pick 1–2 ideas
• Tuesday–Friday: Build, test, publish, or ship
• End of week: Review, archive, or double down
  Repeat next week, unless you’re doubling down on one.

Optional: Use more agents (if you want to go further)

Once the basic loop is working, you can break your system into specialized agents:

• Trend Scanner – finds hot topics from Reddit, Twitter, Hacker News
• Problem Extractor – pulls common issues from reviews or support tickets
• Idea Generator – turns those problems into product ideas
• Validator – checks new ideas against past results or performance

You can run these agents together or on their own — depending on how deep you want to go.