The ROI question nobody likes answering: how do you actually measure AI success?

Most rollouts look great in a demo, then quietly wobble in production because nobody agreed on what “good” means.

What we track when shipping AI agents scale:

Business-side (board-slide friendly)>

• % of flows resolved without escalation
• Cost per successful interaction (not per call/token)
• Adoption and retention: do people actually choose the agent?

Quality side (where things usually break)>

• Accuracy/reply correctness against a golden set
• Faithfulness in RAG (is it grounded or making stuff up?)
• Context relevance - right docs pulled, not random noise
• Hallucination rate - <5% if the stakes are high
• Tool correctness - right API + params, >95% target
• Conversational coherence across turns

Process that keeps you sane>

• Golden dataset (50–500+ real cases incl. edge cases)
• Human-as-judge early, automate later (rules, embeddings, LLM-as-judge)
• Variance checks (run queries 5–10x, if unstable, it’s not production-ready)
• Low-confidence flags with clear fallbacks
• Drift monitoring after launch (logs beat vibes)

Rule of thumb: if self-serve %, cost per success, or adoption is red, then your “success” is just cosmetic.

Curious how others here are doing it:

1. What three metrics decide if you go live or not?
2. Has anyone solved low-overhead hallucination checks?
3. How do you keep model variance from stalling releases?