  Autonomous materials research systems allow scientists to fail smarter, learn
faster, and spend less resources in their studies. As these systems grow in
number, capability, and complexity, a new challenge arises - how will they work
together across large facilities? We explore one solution to this question - a
multi-agent laboratory control frame-work. We demonstrate this framework with
an autonomous material science lab in mind - where information from diverse
research campaigns can be combined to ad-dress the scientific question at hand.
This framework can 1) account for realistic resource limits such as equipment
use, 2) allow for machine learning agents with diverse learning capabilities
and goals capable of running re-search campaigns, and 3) facilitate multi-agent
collaborations and teams. The framework is dubbed the MULTI-agent auTonomous
fAcilities - a Scalable frameworK aka MULTITASK. MULTITASK makes possible
facility-wide simulations, including agent-instrument and agent-agent
interactions. Through MULTITASK's modularity, real-world facilities can come
on-line in phases, with simulated instruments gradually replaced by real-world
instruments. We hope MULTITASK opens new areas of study in large-scale
autonomous and semi-autonomous research campaigns and facilities.
