“6 Fuckups When Building AI Agents”

At first glance, an AI agent looks simple: take GPT-4, write a few prompts, add a UI, wrap it as SaaS, and you’re good to go.
Reality? More complicated. And more expensive.

At Utima.cz, we build AI products for clients and for ourselves. Even with solid know-how and infrastructure, we ran into more dead ends than we expected.

Here are 6 real-world problems we’ve encountered. We’re sharing them so you can avoid them — or at least know you’re not alone.

1. Scope: AI Is Not a Silver Bullet

The problem:
When defining an AI product, the key question is: does AI even make sense here? And more importantly: does it make sense in this specific step?

Example:
A client wants an automatic quotation tool based on product catalogs and incoming requests. Sounds great. But without validation, the AI starts inventing products the client doesn’t even offer. The result? Confusion and loss of trust.

Solution:
AI alone isn’t enough. You need to combine it with full-text search, filtering, and validation at the business logic level.

2. Quality: Garbage In, Garbage Out

The problem:
If your data is incomplete or poor-quality, no model will save you. The same applies if you use an open model instead of RAG, skip the system prompt, or write prompts that are too generic — or over-engineered.

Solution:
Prompts aren’t a game. They’re a product artifact. Test them, log them, version them. Or work with someone who has already launched a few AI projects.

3. Costs: AI Is Not Free

The problem:
High token usage, increased transactions with larger datasets, reranking outputs, multimodal AI — costs escalate quickly. Often before you even notice.

Example:
Our original reranking setup cost tens of thousands per month. After switching to jina.ai, costs dropped to hundreds.

Solution:
You don’t need the smartest AI in the world. The goal isn’t the wow effect — it’s the best price/performance ratio.

4. Payments: Who’s Going to Pay for This?

The problem:
“Let every customer upload their own OpenAI key.”
Reality? UX failure.

On the other hand, building your own billing system and token tracking is expensive and complex.

Example:
A token-based pricing model for a SaaS with 5 plans, dozens of scenarios, and multiple models? Weeks of work — and that’s before scaling.

Solution:
For MVP, forget detailed token measurement. A flat-rate model with basic usage monitoring is more than enough.

5. Measurement: AI Without Metrics Is Invisible

The problem:
Clients often don’t even realize what the AI has delivered. If you don’t show them, they won’t understand why they should continue using it.

Example:
We delivered an AI tool, but without clearly communicating its impact, clients stopped using it. Not because it didn’t work — but because they lacked context.

Solution:
Provide a demo environment, pre/post metrics, and clear impact reporting. And start with education.

6. Updates & Obsolescence: AI Has a Short Lifespan

The problem:
Models change every few weeks. Results, latency, and costs fluctuate. What worked yesterday may be slow, expensive, or outdated today.

Example:
We considered switching from GPT-4o to GPT-5 for a specific dataset. The result? GPT-5 was too slow. Gemini 2.5 Flash ultimately offered a better quality/speed balance.

Solution:
Your architecture must be flexible. Monitor updates, test new models, switch when needed. And account for this in your roadmap.

Final Thoughts

The biggest problems in building AI agents rarely come from the code itself.
They come from poor product design.
Insufficient input validation.
Overestimating AI models.
Lack of measurement.
And missing common sense.

If you want to avoid these dead ends — or recognized yourself in one of them — feel free to reach out. We can help you build it better. Or at least save you a few thousand tokens. 😅