New Survey Maps the Landscape of LLM-Based Agentic Reasoning Across Multiple Domains

A new survey paper on arXiv provides a systematic framework for understanding how large language models power autonomous agents capable of complex problem-solving across a range of real-world applications.

What the Survey Covers#

The research introduces a unified formal language to organize agent frameworks into three distinct compositional tiers: single-agent systems, tool-augmented approaches, and multi-agent coordination. This organizational structure allows researchers and practitioners to compare the design choices, evaluation methods, and underlying capabilities of different agentic systems.

Beyond taxonomy, the survey maps how these frameworks are deployed in critical fields including:

• Scientific discovery and research automation
• Healthcare and clinical decision support
• Software engineering and code generation
• Economic and financial applications
• Society-facing systems
• General-purpose problem-solving tasks

Why This Matters#

As LLM-based agents grow more sophisticated, understanding their reasoning frameworks—the mechanisms that guide how they break down problems and sequence actions—has become increasingly important. The survey highlights that the design of these frameworks shapes both how effectively agents can solve problems and how to evaluate whether they're working as intended.

The field has advanced rapidly, with increasingly complex agent systems moving from research prototypes into production environments. This comprehensive overview helps capture where the technology stands and how different approaches compare in handling diverse scenarios.