On June 11, Google DeepMind announced a $10 million research funding call focused on multi-agent AI safety, in partnership with Schmidt Sciences, the UK government's ARIA moonshot agency, the Cooperative AI Foundation and Google.org. Applications are open until August 8, with awards expected in autumn. Rohin Shah, who leads DeepMind's AGI safety and alignment research, framed the program as a response to a new class of risk that emerges when agents take instructions from other agents without a human in the loop.
Most alignment research to date has studied single models in isolation — one assistant, one user, one task. The new program deliberately targets coordination and collective dynamics: prompt injection chains where one agent feeds malicious instructions to another, scams that propagate through agent-to-agent channels, and emergent collusion or destabilizing behaviors when many agents transact at machine speed. DeepMind's framing is that these failure modes are not edge cases of single-model alignment — they are their own research field.
The timing matters. In the same week, Mastercard launched a machine-to-machine payments protocol with more than thirty partners, and ServiceNow shipped a long-running autonomous desktop agent built on NVIDIA's sandboxed OpenShell runtime. The infrastructure for millions of agents to interact at high velocity is being assembled before the research community has good tools to predict, measure or monitor what happens when they do. A funded call like this is how a frontier lab signals it expects the gap to widen.
For learners: if you are early in your career and looking for a research direction with room to claim ground, multi-agent safety is unusually open. Single-model alignment has thousands of researchers and dozens of benchmarks; agent-to-agent dynamics have neither. The August 8 deadline is short, but the framing of the call — coordination, collective behavior, cooperative AI — is a useful map of where the field thinks the unsolved problems are.