Not all AI models are equal for customer support — and the biggest is not always the best. With Deep Agent Studio, you pick provider and model per agent, and you can switch as needs evolve. Here is how to decide without going wrong.
The 4 criteria that really matter
Understanding quality first: the model must classify fuzzy requests correctly, with typos, mixed topics and short phrasing. That is the decisive test — run your twenty real customer messages through candidates. Then cost per conversation: a few cents difference, times thousands of conversations, changes your ROI. Then latency: every waiting second degrades experience, aim for a first reply within seconds. Finally hosting and compliance: some industries require data hosted in Europe or with a specific provider.
- Understanding of fuzzy, misspelled requests
- Cost per conversation at your real volume
- Latency and first-reply time
- Hosting, compliance and reversibility
The strategy that works: small model first
Start with a cheap mid-range model on your first intent (billing). Measure no-handoff resolution for two weeks. Past 70%, you have already won: no need to pay more for that case. Reserve the most powerful models for complex intents (sensitive claims, multi-step cases) and advisor reply drafts. In Deep Agent Studio, each agent has its own model assignments: conversational inference on one side, RAG indexing on the other.
A small model well framed by a good flow beats a big model left alone — at a tenth of the cost.
Switching models without breaking everything
Your flows, APIs and handoffs do not depend on the model: only the system instruction and assignments change. To migrate, duplicate the configuration onto the new model, replay your three simulator scenarios (nominal, rejected, out of scope), compare classification accuracy, then switch. Keep the old one as backup for a week. Reversibility is what makes the initial choice risk-free.
Common mistakes
- Picking the priciest model “to be safe”: you pay dearly for peace of mind, with no better resolution rate.
- Ignoring latency: an excellent model answering in 15 seconds loses to a good one in 3 seconds.
- One model for everything: mix — cheap for repetitive, powerful for tricky.
- Forgetting indexing: RAG quality depends as much on the embedding model as on the chat model.
FAQ
Do I need a different model per language? No: current models handle French and English together. Just test your real messages in each language.
What if my provider raises prices? Switch assignments in a few clicks, replay the simulator, publish. Your flows stay intact.
Can I self-host the model? Depending on the provider chosen in Deep Agent Studio, yes — check hosting options in your bot's Models tab.
