ElevenLabs Summit: model routing, memory and routines for voice agents
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Today at ElevenLabs Summit NYC, we shared what we’re building next for agents. Here’s what we announced: Fine-tuning: In the coming months, enterprises can work with our team to continually fine-tune our Text to Speech and Speech to Text models, as well as open-source LLMs, on their own data. Interaction Engine: A new system that picks the right LLM for each moment of a conversation. It uses less expensive models for simple requests and frontier models for complex ones like refunds, so you get the right outcome without overspending on tokens. Memory in ElevenAgents: Agents remember what gets mentioned in passing, so businesses can act on it. Routines in ElevenAgents: Agents can now run operations before and after conversations, like pre-screening a mortgage applicant and calling them back, or texting about a confusing bill before it turns into a dispute. ElevenAI: A new AI assistant for work that lets you dictate into any app, takes meeting notes, passes context between agents and people, and provides live assistance during calls.
Voice- builders get per-turn routing to cut spend, cross-conversation memory, and pre/post-call routines inside one platform. Enterprise on own data is promised but not yet dated.
- fine-tuning — Taking a trained model and training it a bit more on your own examples so it gets better at one specific job.
- AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
- LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
- token — The chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.
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