Many teams don’t fail at building AI — they struggle to deploy it with the controls needed for safety, cost, and reliability.
A common pattern: a prototype looks great in demos, then ships without review gates, spend limits, or an easy rollback. The result is usually one of two outcomes: the system gets switched off, or it continues running while quietly accumulating risk.
Three deployment pitfalls we see often
1) No human review path for edge cases
LLM systems produce a “long tail” of uncertain or low-quality outputs. If those outputs reach users without a review path, they can become incident drivers (incorrect guidance, tone issues, policy violations, or brand harm).
2) No cost ceiling
Usage-based AI costs can scale faster than expected. Without rate limits, quotas, and circuit breakers, a system that is inexpensive during testing can become costly under real traffic. (Example: depending on model choice, prompt size, and volume, costs can move from tens to thousands per month.)
3) No baseline
If you don’t measure quality before launch, you can’t detect degradation after launch. “It feels worse” is not an operational metric.
A practical review-gate framework
For most production workflows, four controls cover the majority of risk:
Escalation threshold
Define conditions that trigger human review (e.g., low evaluation score, policy-risk signals, missing required fields, or ambiguous intent). For some systems this can include model scores; for others it’s rule-based or evaluation-based.Cost cap and circuit breaker
Set a daily (or hourly) spend limit. Add an alert before the limit (e.g., at ~80%) and an automatic pause or degradation mode at 100% (e.g., switch to a cheaper model, reduce features, or require human review).Quality baseline
During week one, manually review a random sample (e.g., 1–5% depending on volume and risk) and score it against a simple rubric (accuracy, completeness, tone, policy compliance). Use this as your baseline.Rollback switch
Use a feature flag (or routing switch) that can redirect traffic back to the prior workflow in minutes — without a redeploy.
These controls may add some lead time, but they typically reduce operational risk and rework significantly.
Start small, measure everything
Don’t automate five workflows at once. Choose one with clear inputs/outputs and a safe failure mode. Ship the gates, measure impact, then expand.
Disclaimer: This article is for general informational purposes only and does not constitute legal, financial, or professional advice.