Why You Don’t Need AI Agent Evaluations

People keep asking you the same question:

“How do you evaluate your AI agents?”

Honestly? You don’t.

It’s not that you can’t. It’s not that it’s hard. It’s just that evaluations make you care about things you’ve decided are optional.

Here’s why skipping evals makes sense — assuming you don’t care about maintainability, customers, or sanity.

1. Maintainability sounds like work

Maintainability is overrated.

If you had evaluations, you’d know when:

• behavior regresses
• a “small prompt change” breaks everything
• yesterday’s fix is today’s bug

That creates expectations. Expectations create responsibility.

Without evals, every failure is just unexpected behavior. You don't fix it, you just explain it away.

2. It works. Mostly.

People worry that without evals you don’t know if the system works.

But it does work. It just works.

At least:

• in the demo
• for the one example you remember
• on the prompt you tried yesterday

Evaluations demand answers to annoying questions like:

• “How often does it work?”
• “Did it break after the last commit?”
• “Does it work for anyone else?”

“It just works” is way more flexible.

3. Production is the best test environment

Why test when customers will find the bugs for you?

Production gives you:

• real data
• real crashes
• emotional feedback

Sure, users lose trust. But trust is renewable. Your time is not.

4. Fixing bugs pays better

If your agents were stable, what would you sell?

Evaluations reduce:

• emergency fixes
• hot patches
• prompt rewrites
• late-night "consulting"

An unevaluated system creates endless demand for support and “alignment tuning.”

Evals are great for engineers. Terrible for billable hours.

5. Demos are all that matter

Evaluations don’t help demos.

They don’t:

• animate slides
• impress VCs
• look good on a landing page

One successful run sells the story. Scaling is a problem for Future You.

6. Metrics are boring

Evaluations force questions like:

• Is this actually better?
• Did we regress?
• What improved?

Without evals, progress is measured by:

• vibes
• anecdotes
• a Slack message saying “nice!”

Numbers are precise. Precision is dangerous.

7. Chaos is a vibe

Prompts scattered across:

• Slack threads
• Notion pages
• commented-out code

That’s not technical debt. That’s creativity.

Evaluations force structure:

• versioning
• reproducibility
• reasons for changing things

No thanks.

8. You'd rather not know

Regression tests are anxiety machines.

If you don’t measure performance:

• nothing regressed
• everything is “fine”
• today’s failure has nothing to do with yesterday’s change

Ignorance is a valid engineering strategy.

9. Just hit retry

The Retry button is the best thing to happen to AI.

Why build robustness when users will:

• rephrase
• regenerate
• accept "good enough"

Evaluations turn failure into data. You prefer ambiguity.

10. Can't fail if you don't define success

Once you define "correct":

• people expect it
• failures are objective
• “You think it’s better” stops working

Without evals, quality is subjective. And subjectivity is forgiving.

11. Blame the LLM

Evaluations imply guarantees. Guarantees imply responsibility.

If you never say what it's supposed to do, you’re never wrong. "LLMs are non-deterministic" is the perfect warranty policy.

12. It's not a bug, it's magic

With evals: bugs. Without evals: emergent behavior.

Same thing. Better branding.

Final thoughts

You don’t avoid evaluations because they’re hard.

You avoid them because they would:

• make your system maintainable
• protect users
• reduce chaos
• force discipline
• lower your revenue

And that’s a choice.

AI agents without evals are extremely flexible. Just not in the way you think.