I’ve spent enough time wrestling with conversational AI to know the frustration intimately. You want something that feels natural and spontaneous, but you also need predictable behavior. You want agents that can improvise, but not in ways that derail your entire application. Google’s DialogLab, presented at ACM UIST 2025, tackles this exact problem by building a framework specifically for multi-party conversations.
Most conversational AI work focuses on one-on-one interactions. That makes sense from a research perspective, but it misses how humans actually communicate. Real conversations involve multiple people with shifting roles, interruptions, subgroups forming and dissolving. Think about a team meeting where two people debate while others listen, then someone jumps in with a clarifying question. That’s the kind of dynamic DialogLab tries to capture.
The Scripted vs Generative Problem
Here’s the core tension: scripted conversations are predictable but feel robotic. Purely generative models feel natural but can go completely off the rails. If you’re building a training simulation or a game with multiple AI characters, you need something in between. You want the structure of a script with the flexibility of improvisation.
DialogLab’s approach is to decouple the social setup from the temporal flow. The social dimension defines who’s in the conversation, their roles, relationships, and how they’re grouped. The temporal dimension handles the actual progression: conversation stages, turn-taking rules, when things shift from structured to improvisational. This separation lets you build modular conversations that can adapt without losing coherence.
The framework guides you through three stages: author, test, verify. You configure your agents and their personas, define conversation snippets and transitions, then test everything either in simulation or with live interaction. The visual interface lets you see the conversation timeline and analyze how things played out.
Real World Testing
Google evaluated DialogLab with 14 participants from game design, education, and social science research. They tested three modes: human control (where you direct AI agents), autonomous (agents run themselves), and reactive (agents respond to your inputs). The human control mode scored significantly higher for engagement and generally performed better on effectiveness and realism.
That’s not surprising. What matters is that the tool gives you that level of control when you need it. One participant noted they could “stop the conversation, make changes, and resume immediately,” which is exactly the kind of workflow you want when prototyping complex interactions.
The applications here are pretty broad. Conference Q&A practice where AI agents ask increasingly difficult questions. Debate simulations for education. Game dialogue that adapts to player choices while maintaining character consistency. Customer service training with realistic multi-party scenarios.
What This Means for Developers
DialogLab is open source, which means you can actually dig into how they handle turn-taking, persona management, and the transition between scripted and generative content. For anyone building conversational AI systems, that’s valuable even if you don’t use the framework directly.
The bigger picture is that we’re moving past simple chatbot interactions. Multi-agent systems are where things get interesting and complicated. You need tools that handle that complexity without forcing you to manually script every possible conversation path.
Google mentions future directions like integrating non-verbal behaviors, facial expressions, and photorealistic avatars. That would push this into XR territory where you’re not just managing dialogue but entire simulated social interactions. The framework is part of their XR Blocks ecosystem, so that integration seems likely.
What strikes me most about DialogLab is how it acknowledges the fundamental trade-offs in artificial intelligence design. Pure generation gives you flexibility at the cost of control. Pure scripting gives you control at the cost of naturalness. Real progress comes from building tools that let developers navigate that spectrum rather than forcing them to choose one extreme.