Agint: Agentic Graph Compilation for Software Engineering Agents

LLM-based coding agents are increasingly common, but still face challenges in context management, latency, reliability, reproducibility, and scalability. We present \textbf{Agint}, an agentic graph compiler, interpreter, and runtime that incrementally and hierarchically converts natural-language instructions into typed, \textit{effect-aware} code DAGs (Directed Acyclic Graphs). Agint introduces explicit \textit{type floors} (text $\to$ data $\to$ spec $\to$ code) grounded in semantic graph transformations and a hybrid LLM/function JIT runtime, enabling dynamic graph refinement, reproducible and optimizable execution, and speculative evaluation, and interoperability with existing developer tools. Agint’s typed graph bindings ensure reliability and enables \textit{concurrent composition of concurrent codebases by construction}, supporting accelerated development with smaller, faster models with lower latency, efficient context utilization, and higher throughput. Hierarchical compilation allows scalable graph edits, while the graph structure provides reproducibility and efficient parallel generation. Agint provides a composable unix-style toolchain: \textbf{dagify} \textit{(DAG compiler)}, \textbf{dagent} \textit{(hybrid JIT runtime)}, \textbf{schemagin} \textit{(schema generator)}, and \textbf{datagin} \textit{(data transformer)} for realtime, low-latency code and dataflow creation. Human developers and coding agents refine graphs through the \textit{Agint CLI}, while non-technical users leverage \textit{Agint Flow GUI} with visual editing, conversational refinement, and debugging to promote prototype agentic workflows to production code. This continuous co-creation model lets teams prototype quickly, refine seamlessly, and deploy reliably, bridging natural language, compiler methods, and developer tooling to enable a new generation of composable, team-centric coding agents at scale.