AI Agents in Process Automation: How Insurers Process Complex Workflows in the Background

**Author: Frederik Schrempp, Head of the Research & Development Department at msg life**

Read the original German version here

Summary:

Process automation using agent-based AI enables insurers to perform autonomous, end-to-end background processing of complex business transactions. Through precise interface mapping and targeted function calling, AI agents resolve customer inquiries in a context-sensitive manner and increase the efficiency of existing processes—without any “big bang” risks.

A concrete example of a productive AI solution is the implementation of AI agents for end-to-end automation of business processes. The orchestrating AI independently determines what needs to be done (Identification of Intent), analyzes and classifies incoming customer inquiries (Classification), maps information to system requirements (Interface Mapping), retrieves missing data from structured and unstructured sources (MCP), and executes processes automatically (Function Calling). It is capable of communicating directly with customers or preparing such communication for a case handler. In complex or regulatory-sensitive cases, a “human in the loop” can intervene at any time—with full documentation of all AI steps through what is known as “Thinking Mode.”

The use of AI agents does not require a “big bang” approach

For insurers, this means lower costs, faster processes, an improved customer experience thanks to 24/7 availability, and a noticeable reduction in the burden on internal resources—regardless of skill level. Out-of-the-box, regulatory-compliant solutions are already available, including implemented audit trails for all activities. It’s worth noting that no “big bang” implementation project is necessary: agent-based AI can be expanded iteratively, focusing on the value-adding parts of each process—in line with an agile, usage-oriented approach.

The quality of the existing process logic is crucial

The proper and efficient execution of existing conventional processes by AI agents depends on the quality of the existing process logic. Clean, clearly defined APIs and a good technical description for domain-specific services that can be processed in the background form the foundation for AI agents. In this sense, too, the agents do not replace existing processes; rather, they extend the conventional approach to background processing and build upon it.

Highly Complex Processes Can Be Automated

The technical foundations for behavior are not programmed using rules, but are described through semantic definitions in natural language. Decisions are then derived autonomously and context-sensitively by AI agents.

This makes it possible to significantly expand existing “dark processing” workflows—from customer communication to processing in core systems. Additional parts of the process can now be automated that previously could not be implemented cost-effectively due to high complexity and unstructured, variable data.

A Paradigm Shift in Software Architecture

The use of AI agents is also changing the way software architectures in the insurance industry will be designed and implemented in the future: Traditional workflows and the rigid propagation of data via interfaces are becoming less important.

AI agents are capable of independently analyzing and solving not only repetitive tasks but also complex problems with the help of domain-specific services. Instead of linear workflows, new principles are coming to the fore: processes are increasingly running in parallel, and control is carried out via natural language.

“Family” of AI Agents

The various process steps are not handled by a single AI agent, but by an entire “family” of agents, with one—usually the orchestrator—taking charge of planning, while sub-agents execute individual steps in parallel.

These paradigms enable a more flexible, dynamic, and data-driven design of business processes. They thus form the basis for a new generation of application architectures. This process is already underway!

Conclusion: Expansion Rather Than Replacement of Existing Systems

Those who consciously make the shift from “Play” to “Purpose” will benefit from a key technology for addressing some of the industry’s most important challenges. The use of AI agents plays a central role in this and will lead to a paradigm shift in insurance application architecture. Existing process logic will increasingly be supplemented by an imitation of human decision-making. In doing so, AI recognizes logical relationships and is able to explain every step of the process.

The first step is not about radically replacing existing systems, but rather about extending them. Software is no longer programmed exclusively based on rules; instead, it is defined using natural language. At the same time, this expanded automation occurs iteratively rather than in a “big bang.” Explainability is not an add-on but an integral part of every productive AI solution.

To actively shape this transformation, insurers should rely on standardized solutions that ensure scalability, cost-efficiency, and security. Under these conditions, the targeted use of agent-based AI can already make a significant contribution to value creation in insurance companies today.