Turn: A Language for Agentic Computation
Fuente:
arXiv
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866908874999595008 |
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| author | Kizito, Muyukani |
| author_facet | Kizito, Muyukani |
| contents | 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::<protocol>}) 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_08755 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Turn: A Language for Agentic Computation Kizito, Muyukani Programming Languages Artificial Intelligence Software Engineering 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::<protocol>}) 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. |
| title | Turn: A Language for Agentic Computation |
| topic | Programming Languages Artificial Intelligence Software Engineering |
| url | https://arxiv.org/abs/2603.08755 |