Social
Multi-Agent Systems in Ambient Settings
Rapael Kalandadze · Tatia Tsmindashvili
West Ballroom D
Abstract:
- From Lab to Life: Orchestrating Ambient Agents in the Real World
Rapael Kalandadze (AI Lab, Wandero)
> This talk explores the shift of multi-agent systems from controlled experiments to real-world deployment. We'll examine key challenges, effective strategies, and practical examples of building systems that truly work. This isn't science fiction anymore - it's large-scale system design in action.
2. Teaching Ambient Agents to Understand and Pursue Human Intent
Shirley Wu (Stanford, Microsoft Research)
> This talk explores how long-term alignment strategies can make ambient agent systems more helpful, efficient, and truly human-centered. Shirley Wu presents CollabLLM, a framework that trains agents to look beyond immediate replies by simulating multi-turn interactions and rewarding responses that advance conversations over time. The result: proactive agents that clarify intent, surface missing context, and collaborate more naturally in ambient, ongoing settings.
3. Safety Guarantees for Ambient Agents via Asking for Help
Benjamin Plaut (UC Berkeley, Stanford)
> Most reinforcement learning algorithms essentially rely on trial-and-error: they explore all possible behaviors and see what works well. However, this approach is problematic when some actions are "catastrophic", i.e., irreparable. Ambient computer-use agents have access to many irreparable actions, such as deleting crucial files or sending disastrous emails. We show that designing agents to ask for help in unfamiliar situations improves safety both theoretically and empirically. We believe this is a first step towards a scalable foundation for trustworthy always-on AI systems.
4. WATCHing the Watchers: Real-Time Monitoring for Safer AI Agents.
Drew Prinster (John Hopkins, Yale)
> This talk explores how adaptive monitoring systems can detect, interpret, and respond to failures in long-running AI agents. As agentic systems move from lab to deployment - often operating without constant human oversight - the need for robust, real-time monitoring becomes critical. Drew Prinster presents WATCH, a statistical monitoring framework that rapidly detects performance shifts, distinguishes harmless from dangerous changes, and pinpoints the cause of degradation. This approach enables safer, more reliable deployment of AI in dynamic, high-stakes environments like healthcare or large-scale interactive systems, where false alarms and undetected failures both carry serious consequences.
Panel Discussion - “The Ambient Shift: Redefining Intelligence, Safety & Exploration in Multi-Agent Systems”
Panelists: Yiding Jiang (Carnegie Melon, Google Research),
Clément Romac (Huggingface, Inria), Jindong Wang (William & Mary, Microsoft Research)
> As AI agents move from isolated chats to always-on ambient systems fundamental questions arise: How should these agents explore, generalize, and align with user goals in dynamic, high-dimensional environments? How can we trust them when they act autonomously and concurrently? And what new infrastructures are needed to support this paradigm shift? How can we train LLMs to be fully reliable in production environments?
This panel brings together leading researchers at the intersection of curiosity-driven learning, agent safety, and evaluation to reimagine agent intelligence in ambient settings. This discussion will surface the key redefinitions shaping the future of multi-agent systems.
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