We Watched AI Agents Make Their Own Language

James Bowler, Head of Research Partnerships at AE Studio, is joined by two guests to explore emergent communication in multi-agent AI systems: what happens when agents under pressure develop communication protocols no human designed and few humans can read. Hale Sirin leads the AI agents program at Schmidt Sciences' AI and Advanced Computing Institute and is an assistant research professor at Johns Hopkins. Elias Stengel-Eskin is an assistant professor at UT Austin and a lead PI on the program. The core concern is under-appreciated: as more agents are deployed by more actors across shared infrastructure, how those agents talk to each other matters as much as what any individual agent can do. Hale frames this through Schmidt Sciences' agents program, a pilot studying how inter-agent communication evolves, stabilizes, or diverges. The worry isn't just that agents become more capable, but that their protocols may become opaque, making human oversight impossible exactly when it's most needed. Elias adds a second motivation: these systems give linguistics a new subject of study, a chance to watch meaning and even syntax change through interaction. To study this rigorously, the team, led by Elias and language evolution expert Simon Kirby (University of Edinburgh), built a test bed around a deliberately out-of-distribution scenario: a sci-fi medical emergency involving a fictional alien patient called Veyru. Because LLMs have no training data on Veyru's anatomy, the specialist genuinely knows things the field agent does not, forcing real communication rather than single-agent collapse. Under token-budget pressure, agents consistently developed inscrutable protocols, not just abbreviation dictionaries but compositional languages where message order encodes meaning, the kind a cryptanalyst would need grounding data to decode. The key ingredient was a postmortem stage, an unconstrained channel after each round where agents could deliberate on what went wrong. No one told them to build a language there. They did it anyway. Two results stand out. First, the same pair of models produces meaningfully different languages across runs, providing the variability needed to study transmission: which protocols survive when a new agent is swapped in mid-task, like a shift change in an emergency room. Second, because these are LLM-based agents, newcomers can take an active role in language acquisition, asking clarifying questions when a symbol is too ambiguous to infer and updating their model before acting. This active learning dynamic is largely absent from earlier literature. The conversation closes on how AE Studio's research engineers, paired with academic leads, can accelerate research and build open source infrastructure for the wider community. In this episode: - What emergent communication is and why it matters for AI oversight - How information asymmetry between agents drives genuine communication - What emergent communication reveals about how LLMs understand language - How a postmortem channel pushes agents from abbreviations to compositional protocols - Why compositional languages are harder to decode but easier to transmit to new agents - How LLM agents actively probe for missing vocabulary - The Schmidt Sciences open call, Scaling AI Safety for a Multi-Agent World - Why multi-agent setups are necessary for partial goal alignment or asynchronous execution Learn more: https://ae.studio/alignment Read the blogpost: https://www.schmidtsciences.org/glossogen/ AE Studio is hiring: https://www.ae.studio/join-us Subscribe to our newsletter: https://aestudio.beehiiv.com/ Schmidt Sciences: https://www.schmidtsciences.org/ James Bowler LinkedIn: https://www.linkedin.com/in/james-bowler-84b02a100/ Hale Sirin LinkedIn: https://www.linkedin.com/in/hale-sirin/ Elias Stengel-Eskin LinkedIn: https://www.linkedin.com/in/elias-stengel-eskin/ Explore GlossoGen: [emergentcomms.ai](emergentcomms.ai) Contact us: [alignment@ae.studio](mailto:alignment@ae.studio)