RPA and AI agents in corporate architecture: not instead, but together

A manager of operational technologies at an insurance company RGS (Rosgosstrakk) named Sergey published on Habr a material on whether robotic process automation (RPA) and AI agents based on large language models (LLM) compete with each other or complement each other in the architecture of corporate systems. The author's main conclusion: in the enterprise landscape, these are not competing but mutually complementary technologies, forming two layers of one system.

How RPA differs from AI agents

RPA (Robotic Process Automation) — a technology that in recent years has become a mass tool for automating routine business processes: software "robots" act according to strictly defined scenarios, repeating the same steps on structured data — for example, transferring data between systems, filling out forms based on templates, generating reports according to a fixed algorithm. AI agents based on LLM are organized fundamentally differently: they reason over unstructured data — texts, images, arbitrary user requests — and are capable of making decisions in situations for which no rigid scenario was prescribed in advance.

Why choosing one technology is a choice without a choice

The author formulates the key thesis of the material as follows: if an organization is forced to choose only one of two approaches, it inevitably loses in efficiency — because RPA and AI agents solve different classes of tasks, and replacing one with the other means either overpaying for LLM where a simple templated script would suffice, or running into rigid RPA limitations where interpretation of unstructured information is required.

Key theses of the article:

• The author is Sergey, head of operational technologies at RGS (Rosgosstrakk).
• Publishing platform — Habr, AI section.
• RPA — effective for repeated processing of well-structured, repetitive tasks.
• AI agents on LLM — effective for working with unstructured data and tasks requiring reasoning.
• Conclusion — in enterprise architecture, these are two mutually complementary layers, not competing alternatives.

How RPA and agents work together in practice

According to the author, the most productive architecture for a large organization is not replacing one technology with another, but building a system where each layer solves tasks it is best suited for. RPA robots continue to perform mass, predictable, easily verifiable operations — where speed, cost-effectiveness and one hundred percent repeatability of the result matter. AI agents are engaged where the process requires context interpretation: analyzing customer requests written in free text, making decisions in atypical situations, synthesizing information from disparate sources before passing a structured result downstream — including to the same RPA robot for final execution.

For the insurance industry where the author works, such a hybrid architecture is especially relevant: processing claims for payouts, assessing insurance cases and working with customer inquiries combine both strictly regulated, formally documented steps ideal for RPA, and elements requiring understanding of natural language and context of a specific situation — the domain of AI agents. The material on Habr apparently examines a concrete case of applying such a combined architecture at RGS, demonstrating why these two technologies, which are often opposed to each other in public discussions, in practice turn out to be parts of one production chain, not mutually exclusive alternatives.

RPA as a category of corporate software began to spread massively in the mid-2010s, when companies worldwide faced the need to quickly automate inherited (legacy) processes without having the ability or budget for complete restructuring of internal information systems. Robots simply imitated human actions in existing interfaces — clicking, copying, pasting data — which made implementation fast but inflexible to any changes in the systems themselves. The emergence of AI agents based on LLM a decade later does not abolish this accumulated automation infrastructure, but gives companies like RGS the ability to layer over it a more flexible layer capable of working with exceptions and non-standard cases that classic RPA scenario simply could not handle and passed to a human operator.