Picture this: It's late-October. Black Friday is coming. I want to buy a Sactional—you know, the age appropriate adult version of "The Lovesac" beanbag we all had in our college dorm room. This couch takes up your entire living room and is so comfortable it makes you feel like a giant hug whilst watching "Married At First Sight" (Don't judge me).

I open ChatGPT and ask: "Find me the best deal on a sactional sofa for Black Friday."

ChatGPT gives me... garbage. It can't look up prices from Costco and struggles with the different combinations from the retailer. The prices are outdated. One of the "deals" is actually more expensive than the regular price. It's like asking a librarian for book recommendations and they hand you a phone book from 1987.

I think: "There's a product here."

So I start building FridayFinds. AI-powered price discovery. Real-time deals across retailers. The thing that ChatGPT should be able to do but can't.

I tell my AI agents what I want: "Just tell me who has the best price."

Simple, right?

The Scope Creep (Featuring My AI Agents)

I open up Cursor and I explained the vision to my AI agents. If you read my post on how agents are pretty much Meeseeks you'll know to say they were excited is an understatement.

And then these lil blue guys started suggesting things.

"Ooh, yes! I'm Mr. Meeseeks, look at me! You know what would be amazing? Price tracking! Users could save items and get alerts when prices drop! I can do that! I'm Mr. Meeseeks!"

"Ooh! Oh! And deal scoring! We could build an ML model that ranks which deals are actually good! I'm Mr. Meeseeks, look at me, I can build models!"

"What about forecasting?! I can predict future prices and tell users when to buy! Ooh, yes, I can do that! I'm Mr. Meeseeks! Look at me!"

"You'll need 13 different shopping APIs! Sovrn Commerce, PriceAPI, Rainforest API, ShopSavvy, CJ Affiliate, AWIN! I can orchestrate all of them! I'm Mr. Meeseeks! I can integrate APIs! Ooh, I'm so helpful!"

"User authentication! Database optimization! Historical queries! A notification pipeline for alerts! An event streaming system for real-time updates! I can do all of it! I'm Mr. Meeseeks and I exist to serve! Ooh, yes! I can build these things! Look at me!"

This is the moment where I realize: my AI agents are planning like engineers, not like product managers.

They're not wrong. All of it would be cool. All of it would be useful. But it's not what I asked for. I asked for: "Who has the best price?"

Everything else is scope creep dressed up as comprehensiveness.

Day 2: The Moment I Stopped Listening to My Agents

I'm two days in. I've got the skeleton of the data layer built. My agents are excited about the 13-API orchestration layer. The complexity they're describing is real—rate limiting across different APIs, data normalization, fallback logic.

And I realize: I don't need any of this.

I test Exa AI. Semantic search across the web. One API call. It searches across all retailers semantically. It works. Beautifully.

One API instead of thirteen.

Then I think about tracking. Why do users need to save sectionals? They need to find the best deal right now. They're buying a sectional for Black Friday. They don't need to track prices for six months.

I ask my agents: "Do we really need user authentication for MVP?"

Them: "Well, technically no, but—"

Me: "Then we're not building it."

Me: "Do we need price forecasting?"

Them: "That would require 60+ days of historical data and an ML model, so—"

Me: "We're not building it."

Me: "Deal scoring?"

Them: "We could train a model on—"

Me: "Users can see the prices. They can decide. We're not building it."

This is the conversation that should happen on Day 2, not Day 45.

The Real Problem: Nobody Stopped My Agents

Here's what I realized: my AI agents weren't trying to sabotage me. They were doing what they're trained to do. Be comprehensive. Build infrastructure. Think ahead. Plan for edge cases. Design systems.

That's good engineering. But it's not good product development.

Good product development says: "What's the smallest thing that solves the problem?"

My agents said: "What's the most complete thing we could build?"

And I let them lead. That's on me.

So on Day 2, I did something my agents weren't expecting. I simplified their work.

I said: Here's what we're actually building.

• Two MCP tools (search_offers, compare_retailers)
• One data provider (Exa AI)
• One database (Convex for caching)
• React UI embedded in ChatGPT

That's it.

Everything else—tracking, forecasting, alerts, complex orchestration—moves to Sprint 2. Or Sprint 3. Or never.

Why This Matters: The Sprint Boundary

This is the thing about building with AI agents: they don't stop unless you stop them.

Claude will suggest features. Cursor will implement complex architecture. They'll do it because that's what they're trained to do. They're optimizing for "comprehensive" not "shipped."

Without a sprint boundary, without a moment to stop and say "wait, do we actually need this?", you end up building everything your agents suggest.

And you never ship.

Sprint 0: Planning & Infrastructure

• I explain the vision to my agents
• They suggest everything
• I write it down
• We have a spec
• We setup the base tooling (ex: Express MCP server, Convex DB)

Sprint 1: Building

• We build against the spec
• My agents execute
• I don't change requirements mid-sprint
• We build what we planned

Sprint 1 → Sprint 2: The Reflection Point

• I step back
• I ask: "Did we learn something?"
• I ask: "Do we actually need all this?"
• I update the plan

What Changed at the Boundary:

Original Spec (Post-Agent-Enthusiasm)
├─ 13 APIs orchestration
├─ User tracking infrastructure
├─ Deal scoring ML model
├─ Price forecasting engine
├─ Notification pipeline
├─ Historical data collection
└─ Estimated effort: Months of work

Updated Spec (Post-Reflection)
├─ 1 API (Exa)
├─ 2 MCP tools
├─ Convex for caching
├─ React UI
├─ NO tracking
├─ NO forecasting
├─ NO alerts
└─ Estimated effort: 1-2 more sprints

The agents didn't complain. They pivoted. They said: "Okay, here's how we build this smaller thing better."

What Would Have Happened Without the Sprint Boundary

If I didn't have a moment to stop and say "wait," here's what happens:

Early Phase: Planning

• Agents are excited
• Scope is massive
• I'm committing to everything

Middle Phase: Implementation

• Agents start building the 13-API orchestration layer
• I'm watching, thinking this seems complex but necessary
• More infrastructure gets built—user tracking, database optimization
• It's starting to feel bloated, but I'm committed to the spec

Late Middle Phase: Realization

• Somewhere in here, I would have realized: this is too much
• But by then, I'm too invested
• I've got partial implementations of features I don't need
• My agents have built infrastructure that only makes sense with the full scope

End Phase: Crisis

• Now I have a choice:
  • Push through and ship something bloated (bad product)
  • Rip out half the work and replan (wasted time and effort)
  • Keep the stuff I built but don't use it (technical debt)

All of these are worse than the simple thing I actually built.

The sprint boundary gave me permission to change my mind before I was locked in.

Why Agile Works (For AI-Assisted Development Especially)

Here's the thing about working with AI agents: they're going to suggest more than you need. That's literally their job—to explore possibilities, suggest comprehensive solutions, think about edge cases.

You need a moment to say: "No, simpler. We're shipping the MVP first."

Agile gives you that moment. It's called the sprint boundary.

You don't plan for 12 cycles with your agents and then execute 12 times. You plan for 1 sprint. You execute for 1 sprint. Then you ask: "Did we learn something?"

If yes, you adjust. If no, you keep going.

Sure, it's acceptable to have a high-level vision. I keep mine in an "epic spec" and a PRD. But these are always mutable based on what I learn while executing with my agents.

Now at the end of each sprint I ask that key question. And the answer almost always is: "Yeah, we don't need 13 APIs. We don't need user tracking. We don't need forecasting. We need to ship."

Without a sprint boundary, I would built everything my agents suggest. And I would ship entirely too late.

The Takeaway

Building with AI agents is amazing. They're fast, they're thorough, they suggest things you wouldn't have thought of.

But they don't have product instinct. They don't know when to stop. They don't feel the pressure of "ship now vs. perfect later."

You do.

Use sprints as a way to inject product thinking into the process. Plan for 1 week with your agents. Execute for 1 week. Then stop and ask: "Did we learn something? Do we need to adjust?"

The boundary isn't a limitation. It's where you stay in control.

Without it, your agents will build a beautiful, comprehensive, overcomplicated thing that never ships.

With it, you'll build a simple, focused MVP that actually solves a problem.

And that's the difference between "I'm building something cool" and "I shipped something people want."

Now go build your ChatGPT App. And don't let your agents build more than you asked for.