Original Reddit post

I’ve been talking to a lot of teams building voice agents lately, and there’s a pattern I keep seeing. Early stage:

• You train on internal scripts
• Then a handful of client calls
• Accuracy jumps fast and Confidence grows Then around 1k–5k conversations something strange happens… Performance plateaus. Not because the model is bad but because the data distribution is too narrow. Common issues I see: 1️⃣ Overfitting to one industry If your early clients are dental clinics, your agent starts sounding like it only understands dentistry. 2️⃣ Polite-user bias Most early calls are cooperative users. Real-world production traffic includes interruptions, sarcasm, frustration, accents, background noise, etc. 3️⃣ Clean-call bias Client sample calls are usually curated. Real traffic has mic clipping, crosstalk, hold music, poor connections, etc. 4️⃣ Workflow tunnel vision The agent learns the “happy path.” It struggles when users jump contexts mid-call. 5️⃣ Demographic under-representation Voice models degrade quickly without accent and speaking-speed diversity. The interesting part I’ve found is that people usually try to fix this with more of the same data. But scaling 2k similar calls to 20k doesn’t increase robustness, it just increases confidence in a narrow band. The teams that break through that plateau usually:
• Intentionally expand distribution
• Introduce structured edge-case scenarios
• Diversify speaking profiles
• Separate “logic training” from “noise training” Curious where others have hit that ceiling and what solved it for you? submitted by /u/Khade_G

Originally posted by u/Khade_G on r/ArtificialInteligence