MOSAIC: Modular Orchestration for Structured Agentic Intelligence and Composition

2026-05-30 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

MOSAIC (Modular Orchestration for Structured Agentic Intelligence and Composition) is a new structured agentic framework designed for memory-grounded model selection and workflow construction in automated data science. Addressing limitations of traditional AutoML and unstructured LLM agents, MOSAIC builds a semantic task profile, retrieves prior cases and source-code modules, and generates a "blueprint" for modelling components and execution requirements. This approach grounds LLM-based code generation in evidence, enabling validation through execution and refinement via diagnostic feedback, training traces, task metrics, and a failure-aware reinforcement learning policy. Instantiated on financial time-series forecasting and generation, MOSAIC demonstrated improved task performance, execution success, and decision traceability compared to existing AutoML and agentic baselines, satisfying criteria like predictive accuracy and risk.

Key takeaway

For Machine Learning Engineers developing automated data science solutions, MOSAIC offers a robust framework to overcome the limitations of unstructured LLM agents and rigid AutoML systems. You should consider adopting its structured, blueprint-driven approach to enhance model selection, improve execution reliability, and ensure traceability, especially for tasks requiring complex criteria like financial risk assessment. This can lead to more verifiable and reusable agentic workflows.

Key insights

MOSAIC structures LLM-based agentic model selection through blueprints, improving automated data science performance and traceability.

Principles

• Automated data science is a structured model-selection problem.
• Ground LLM code generation in retrieved evidence.
• Validate models through execution and diagnostic feedback.

Method

MOSAIC profiles tasks, retrieves prior cases and modules, then constructs a "blueprint" for components and execution. It validates candidates via execution and refines using RL policy and diagnostics.

In practice

• Apply to financial time-series forecasting.
• Improve model selection for complex criteria.
• Enhance traceability in agentic workflows.

Topics

• Agentic AI
• Automated Machine Learning
• Model Selection
• Financial Time Series
• LLM Code Generation
• Reinforcement Learning

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.