Agentic AI and Retrieval-Augmented Models in Straight-Through Underwriting

Jul 16, 2026

Artificial intelligence is reshaping actuarial practice, especially where decisions must reason over unstructured documents, heterogeneous data, and regulated workflows. The paper evaluates architectures spanning rule-based automation, large language models, retrieval-augmented generation (RAG), and multi-agent “agentic” systems for straight-through underwriting. A synthetic yet realistic environment is built for small commercial Business Owner Policies (BOPs). Three underwriting pipelines are compared: single-LLM, naive RAG, and an Agentic RAG multi-agent approach with targeted retrieval and explicit multi-step rule evaluation, achieving the strongest results in multi-step and missing-information cases.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages26

Reading Time1 h 6 min

Abstract

Introduction

From rule-based automation to LLMs

Retrieval-Augmented Generation (RAG)

Agentic AI and multi-agent orchestration

Motivation for actuarial underwriting

arXiv :2607 .07858v 1 [ cs .AI] 8 Jul 2026 Agentic AI and Retrieval-Augmented Models in Straight-Through Underwriting Robert Richardson, Josh Meyers, Brian Hartman, and David Sandberg Brigham Young University Abstract Artificial intelligence (AI) is beginning to reshape actuarial practice, particularly in domains that require reasoning over unstructured documents, heterogeneous data sources, and regulated decision workflows. Actuaries now face a design space that ranges from traditional rule-based automation to large language models (LLMs), retrieval-augmented generation (RAG), and multi-agent “agentic” systems that plan, retrieve, call tools, and reflect. This paper examines how these emerging architectures can support actuarial priorities such as transparency, auditability, and human-in-the-loop governance, with a focus on straight-through decision processes. To make these ideas concrete, we develop and analyze an agentic AI framework for straight-through underwriting of small commercial Business Owner Policies (BOPs) . We construct a synthetic but realistic experimental environment and compare three underwriting pipelines: (i) a single-LLM baseline, (ii) a naive RAG system, and (iii) a multi-agent “Agentic RAG” pipeline that combines targeted retrieval, third-party data checks, and explicit multi-step rule evaluation. The agentic system performs best overall, with the largest gains in multi-step and missing-information scenarios, where structured retrieval and reflection help the model avoid unsupported straight-through decisions. Keywords: Retrieval-Augmented Generation (RAG); Human-AI Interactions; Synthetic Data Generation; Insurance Automation; 1 Introduction Artificial intelligence (AI) has undergone rapid evolution in recent years, moving from rule-based automation toward models with increased adaptability and autonomy. Traditional automation systems execute predefined workflows, often lacking flexibility when confronted with unstructured or unforeseen data. In contrast, large language models (LLMs) such as those powered by transformer architectures have enabled richer capabilities: they can process unstructured natural language inputs, generate fluent text, and perform a variety of downstream tasks. The distinction between rule-based automation and LLM-based systems should not be interpreted too sharply. In many applications, explicit rules, statistical models, and LLMs often play complementary roles. A rule engine is well suited to apply known decision logic once the relevant facts are available. An LLM, by contrast, may be useful when those facts are embedded in messy business descriptions, underwriting notes, or other unstructured text. In some cases, LLMs may also help surface recurring patterns or exceptions that can later be reviewed by domain experts and translated into explicit underwriting rules. The practical question is therefore not whether rules or LLMs are better, but how each can be used in the parts of the workflow where it is most reliable. One of the pivotal developments in this space is the technique known as retrievalaugmented generation (RAG) . In the foundational work by Lewis et al. (2020), RAG models are defined as combining a parametric memory with a non-parametric retrieval mechanism. The authors show that such a hybrid architecture improves performance on knowledge-intensive tasks by enabling the system to access external factual content rather than relying solely on internal model parameters. Subsequent surveys and reviews reinforce that RAG architectures help mitigate phenomena like “hallucination” (where the model asserts facts not grounded in the external world) and improve factuality and traceability (Gupta et al. , 2024) . While RAG enhances knowledge access, it remains a relatively narrow paradigm: retrieval plus generation. The next frontier is what many authors term “agentic AI,”which are systems that behave more like autonomous agents, capable of setting su

FAQs

What does the paper focus on in actuarial practice?

It examines how agentic AI and retrieval-augmented architectures can support actuarial priorities like transparency, auditability, and human-in-the-loop governance in straight-through underwriting workflows.

How are the underwriting approaches compared in the study?

The paper compares three pipelines: a single-LLM baseline, a naive RAG system, and a multi-agent “Agentic RAG” pipeline that combines targeted retrieval, third-party data checks, and explicit multi-step rule evaluation.

Why does the agentic system perform better in the experiments?

It delivers larger gains in multi-step and missing-information scenarios because structured retrieval and reflection help the model avoid unsupported straight-through decisions.

Jul 16, 2026

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