MOSAIC: Modular Orchestration for Structured Agentic Intelligence and Composition

Automated data science is a structured model-selection problem: a solution must choose data transformations, feature representations, architecture, training procedure, evaluation protocol, and refinement strategy appropriate for a given task. AutoML systems automate parts of this process, but typically search within predefined pipeline, model, and hyperparameter spaces. LLM-based agents offer greater flexibility through retrieval, code generation, and execution feedback, yet their modelling decisions are often unstructured, difficult to verify, and hard to reuse across tasks. We introduce MOSAIC (Modular Orchestration for Structured Agentic Intelligence and Composition), a structured agentic framework for memory-grounded model selection and workflow construction. Given a task and dataset, MOSAIC builds a semantic task profile, retrieves relevant prior cases and source-code modules, and constructs a blueprint: an intermediate representation specifying the selected modelling components, their composition, interface constraints, and execution requirements. This blueprint turns model selection into a staged, context-grounded search process and grounds LLM-based code generation in retrieved evidence rather than unconstrained synthesis. Candidate models are validated by execution and refined using diagnostic feedback, training traces, task metrics, and a failure-aware reinforcement learning policy for long-horizon improvement. We instantiate MOSAIC on financial time-series forecasting and generation, a demanding application where models must satisfy predictive accuracy, distributional fidelity, execution reliability, and downstream financial criteria such as risk and tail behaviour. Experiments against AutoML and agentic baselines show that MOSAIC improves task performance, execution success, and decision traceability, demonstrating the value of treating automated data science as structured, reusable, and execution-grounded model selection. Our code is publicly available at https://anonymous.4open.science/r/MOSAIC-8778/.