Turn: A Language for Agentic Computation

arXiv:2603.08755v1 Announce Type: new
Abstract: We present textbf{Turn}, a compiled, actor-based programming language — statically typed for schema inference, dynamically typed at the value level — for agentic software: programs that reason and act autonomously by delegating inference to large language models (LLMs). Existing approaches augment general-purpose languages with frameworks, encoding critical invariants (bounded context, typed inference output, credential isolation, durable state) as application-level conventions rather than language guarantees.
Turn introduces five language-level constructs that address this gap. emph{Cognitive Type Safety} makes LLM inference a typed primitive: the compiler generates a JSON Schema from a struct definition and the VM validates model output before binding. The emph{confidence operator} enables deterministic control flow gated on model certainty. Turn’s emph{actor-based process model}, derived from Erlang, gives each agent an isolated context window, persistent memory, and mailbox. A emph{capability-based identity system} returns opaque, unforgeable handles from the VM host, ensuring raw credentials never enter agent memory. Finally, emph{compile-time schema absorption} (texttt{use schema::}) synthesizes typed API bindings from external specifications at compile time; the texttt{openapi} adapter is shipped with texttt{graphql}, texttt{fhir}, and texttt{mcp} in active development.
We describe the language design, type rules, schema semantics, and a Rust-based bytecode VM, and evaluate Turn against representative agentic workloads. Turn is open source at https://github.com/ekizito96/Turn.