11 Brutal Truths About Deploying AI Agents in Production

Shipping AI agents is the fastest way to discover how messy “intelligence” really is. These are the hard lessons that stuck after deploying mission-critical agent pipelines into production.

1. Prototypes are easy—production is brutal

Demos are fun. Production means handling state, validation, monitoring, and fallbacks.
It’s more software engineering than AI.

2. LLMs are deterministic... until they’re not

The same prompt can break depending on model version, temperature, or context.
Predictability is a myth.

3. 80% is easy—the last 20% will humble you

Getting to “good enough” is fast.
Going from 80% to 95% is where your sprint turns into a marathon.

4. Without guardrails, multi-agent pipelines collapse

Strong validation, retries, and fallbacks are non-negotiable.
Agents are only as reliable as their weakest loop.

5. Token costs will shock you

Debugging, retries, and experiments balloon usage.
Control your prompt versions or bleed cash.

6. LLMs are smarter—still not reliable

Hallucinations happen. Instructions get ignored.
Trust without verification kills user confidence.

7. Open source is exciting—not plug-and-play

OSS tools are promising but flaky.
Expect missing docs, rough APIs, and inconsistent performance.

8. The best agents are opinionated

General chatbots are mid.
Narrow, well-scoped, domain-rich agents win.

9. Accuracy is not the real KPI

Measure time saved, ops automated, and user NPS.
Precision is a vanity metric if business value lags.

10. Fallbacks are your best feature

“I don’t know” + escalate to a human beats confident hallucinations every time.

11. Debugging will break you

“Where’s this data from?”
“Why’s it failing only here?”
You’re no longer debugging code—you’re chasing ghosts.

AI agents are definitely the future. But production is a battlefield. Plan accordingly—and build with brutal honesty.