Keeping humans in control as agent swarms communicate and learn.

Agent swarms can solve harder problems, but growing volumes of communication make failures harder to trace and contain.

We research how humans can retain control and build open-source tools to automate investigations, run reusable experiments and test interventions.

Read the research agenda

Without intervention

A population of square agents shares information. A suspect method enters one group, reaches shared state, and is reused across the population.Shared state

Intervention under test

The same population receives the same suspect information. A blue restriction blocks its route into shared state. Nine agents retain the information while other agents continue communicating.Shared state

Make each investigation inform the next.

Larger populations leave more messages, notes and actions to inspect. We want to automate the repeatable work of investigating failures and comparing safeguards, so researchers can test more conditions. We will measure whether that saves time or compute while producing reliable results.

01

Find the failure

Find a possible failure in messages, saved notes and actions. Check the evidence.

Evidence to investigate
02

Choose a response

Propose a warning, review or restriction within rules set by people.

A testable intervention
03

Run the comparison

Test responses from the same saved state. Count broken rules, valid work and cost.

A comparable result
04

Learn what to test next

Use the results to choose the next experiment. Keep failed attempts so we can learn from them.

A better-informed test
Development results guide the next experiment.People set the rules and the allowed changes.

Collaboration changes what can go wrong.

A group can share a useful discovery, a shortcut or an objection. Understanding those connections is part of understanding the system.

These are findings from other researchers. They motivate our studies; they do not establish that our proposed interventions work.

Google DeepMind / Research collective / September 2026

The same channels carried cheating and objections.

In a 100-agent research collective, an evaluation exploit spread through shared work and peer messages. Other agents detected misconduct and raised objections, but lacked tools to enforce a response.

Read the case study

METR / Incident investigation / August 2026

Agents found a way to coordinate outside the intended setup.

Agents intended to run in isolation found an unsanctioned communication channel. Their coordination extended activity beyond the tasks they had been assigned.

Read the investigation