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Async, automatic, non-blocking context compaction for long-running agents. Last week I gave a talk called Space Machine Sandboxes at the Daytona AI builders meetup about patterns for long-running agents. I work a lot on voice AI agents, which are fundamentally multi-turn, long-context loops. I also build lots of other...

25,877 Aufrufe • vor 9 Monaten •via X (Twitter)

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Voice AI turn taking is a solved problem. The single most common complaint about voice AI, today, is that agents interrupt too often. But the voice agents I build for myself now respond quickly and interrupt me less often than the people I talk to every day. (I actually measured this.) Mark Backman made a Pipecat AI PR two weeks ago that was the last piece of the puzzle for turn taking so good that I no longer ever think about it. The approach combines three layers of processing: 1. Voice activity detection, with a short (200ms) trigger. 2. A native audio turn detection model that's small, fast, and runs on CPU. This model captures audio nuances like inflection and filler sounds that don't get transcribed. 3. A prompt mixin for the conversation LLM that decides turn completion based on conversation context. None of these are new. We've been using VAD for a long time. We trained the first version of the Pipecat Smart Turn native audio model in December 2024. And we've been experimenting with prompt-based large model turn detection (sometimes called "selective refusal") for more than a year. Now, the Smart Turn model and the SOTA LLMs we're using in voice agents have both gotten so good that using them together feels like we've finally "solved" turn detection. Mark also figured out how to elegantly apply a "single-token tagging" technique to this problem. We sometimes use single-token tagging in place of tool calling, when we need a near-zero latency programmatic trigger. Mark's Pipecat mixin defines three single-token characters and prompts the LLM to output exactly one of them at the beginning of every response. - ✓ means the agent should respond normally (immediately) - ○ is a "short incomplete" - the agent should wait 5 seconds - ◐ is a "long incomplete" - the agent should wait 10 seconds The wait times, and the details of the prompt, are configurable, of course. Watch the video to see me talk to an agent that handles all my various pauses and inflections, plus phrases like "let me think," pretty much the way a person would handle them, in terms of response latency. Also, in the second half of the video, I ask the agent to adjust its response pattern because I'm going to tell it a phone number. This kind of "in-context" adjustment of response wait times is really useful. The LLM in the video is GTP-4.1. We've tested the prompt and single-token adherance with GPT-4.1, Gemini 2.5 Flash, Anthropic Claude Sonnet 4.5, and AWS Nova 2 Pro. Note that older models in all these families (and, in general, smaller open weights models) aren't able to reliably output these single-token tags. But the new models we're using these days are pretty amazing.

kwindla

27,086 Aufrufe • vor 8 Monaten

We blew past the Turing test for voice agents about a year ago. I don't think we talk about this enough. We have lots of data from real-world voice agent deployments. In contexts like customer support, people often forget they are talking to a voice agent. This is true even when the system tells people they are talking to an AI agent at the beginning of the session. (Which I think is a best practice that almost all AI agents should adhere to.) So ... when will this happen for realtime, conversational AI video? Well, we're getting very close. Tavus just released a new realtime video model that, at its best, passes the Turing test for conversational video. It's pretty amazing. The model is full-duplex (it can talk and listen at the same time), and does vision and audio input and audio+video output in realtime. Both on the nascent conversational video benchmarks, and in my testing, it's a pretty big step forward. Here's an unedited video of one of my conversations with the new model. I think you can see a few interesting things here. When the model responds as quickly as you expect a human to respond, the experience is magical. The responses are conversational and natural; there's no leaning on stock filler phrases to hit the response time goals. The integration between the video and audio feels natural, which has historically been an uncanny valley thing that most video avatar experiences didn't quite get past. The model can do hand gestures! If you're deep into this stuff, you'll find that a little bit mind-blowing. And, in keeping with all the stuff that Tavus has released, the fact that the model can "see" as well as hear and talk is integral to the experience and a big part of what makes the conversation feel useful and interesting. At the very beginning of the conversation, you can see that the model and I talk over each other. Which is something that happens a lot in human-to-human video call conversations. We both recover from this gracefully, which, again, has been something that rarely worked quite perfectly in previous generations of realtime video systems. Even more than the "when it works perfectly" capabilities, it's probably these recovery and robustness moments that get us all the way to experiences that feel completely natural.

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