Multi-Agent Systems – The Engine of Claims Automation
What Are Multi-Agent Systems (MAS)?
A multi-agent system is a collection of autonomous AI agents that work together to achieve a goal.1 Unlike a monolithic AI, each agent specializes in a specific task (e.g., document understanding, fraud detection, pricing) and coordinates with others to solve complex, multi-step problems.2
The core features of multi-agent systems include autonomy, decentralization, collaboration, adaptability, and scalability, making them ideal for dynamic domains like insurance claims.4 Each agent can operate independently, making decisions for its designated task without waiting on a central controller. Agents collaborate by sharing information and handing off tasks; for example, an OCR agent extracts data from a claim form and passes structured data to a validation agent, which then passes the results to a decision agent. The system scales flexibly because new agents can be added or existing ones modified (e.g., adding a new agent for a new product line) without requiring a re-architecting of the entire system.
Importantly, this architecture aligns well with the functional structure of an insurance claims department. It handles different claim types and data sources in parallel, much like human teams are often organized with specialists for various functions (e.g., one adjuster, one fraud examiner, one subrogation specialist). This makes the MAS concept powerful and intuitive to implement within an existing business framework.
MAS in Insurance Claims
In the context of claims, a multi-agent system functions as a digital workforce. The complex process of adjudication is broken down into a series of subtasks, with a dedicated agent assigned to each. A typical configuration might look like this:
Intake Agent: Uses Natural Language Processing (NLP) and computer vision on First Notice of Loss (FNOL) submissions (text, images, video) to populate the claim in the system and perform an initial risk assessment.
Triage Agent: Classifies the claim by type (e.g., auto, property, liability) and complexity (simple, complex) and routes it to the appropriate workflow or human team.
Document Agent: Extracts and validates data from attachments like accident reports, medical records, and invoices using advanced Optical Character Recognition (OCR) and NLP.
Rules & Coverage Agent: Checks policy coverage, jurisdictional rules, and endorsement data against the core policy administration system to filter out ineligible claims or flag coverage issues.
Fraud Detection Agent: Analyzes patterns in claim data (text, image metadata, claimant history, network connections) to score fraud risk and flag suspicious claims for review.
Valuation Agent: This type of agent uses predictive models and computer vision to estimate settlement amounts or repair costs, for example, by analyzing photos of vehicle damage.
Decision Agent: Integrates the inputs from all other agents to approve, deny, or escalate the claim, generating a natural-language explanation for its recommendation.
Customer Agent: The customer agent communicates with the claimant via chat or email, answering questions, providing real-time status updates, or gathering additional information automatically.
This division of labor is precisely the agentic AI approach. A MAS can dedicate one agent per primary claim function, resulting in substantial efficiency gains. For example, customer communication—traditionally a significant drain on adjuster time—can be handled by an AI chat agent that answers FAQs or explains outcomes. Similarly, an AI agent can fully automate document processing, significantly reducing the need for manual data entry.
Figure 1 Architectural Comparison: Monolithic vs. Multi-Agent Claims Processing
Figure 1 Illustrates the MAS advantage. A single LLM-only agent takes one user prompt and returns one output, which is useful but limited. In contrast, an agentic pipeline breaks a claim task into subtasks (e.g., text recognition, rule checks, context lookup, calculation), each handled by specialized tools (such as OCR engines, databases, and APIs). This makes the system far more powerful, versatile, and transparent. In practical terms, MAS enables insurers to leverage multiple AI modalities—vision, language, and predictive analytics—within a single, auditable workflow.
Key Role of MAS in Service-as-Software
Service-as-software relies on MAS because delivering a complete service, such as end-to-end claims handling, requires orchestrating multiple, distinct capabilities. The MAS approach provides the ideal structure for this orchestration. It ensures:
Flexibility: Individual agents can be updated, improved, or replaced without disrupting the system.
Transparency: Each agent’s task is clearly defined and its performance is traceable, eliminating the "black box" problem of monolithic AI.
Resilience: If one agent fails or requires human review, the others can continue their work, preventing a total system shutdown.
Regulatory Alignment: The modular and auditable nature of MAS is critical for meeting regulatory and audit needs. Each decision point is handled by a defined agent, whose inputs, outputs, and internal logic can be logged, monitored, and validated to meet regulatory requirements, such as those imposed by the FCA3 or the EU AI Act.4
Furthermore, MAS systems are self-improving. Agents can learn and adapt over time. For instance, a claims agent can utilize techniques such as Deep Reinforcement Learning (DRL) for continual learning to refine its decisions based on outcomes.4 Multi-agent architectures also support gains from scale, where improvements in one agent (such as a more accurate fraud model) benefit the entire workflow immediately. This compounding effect is why experts believe MAS amplifies AI’s impact across the organization.
Conclusion and Next Steps
Multi-agent systems are the conceptual and technical backbone of claims service-as-software. Insurers can automate complex end-to-end services by decomposing the complex claims process into a network of interlocking, specialized AI agents. In the next blog, we will outline a detailed lifecycle blueprint for implementing a MAS-powered claims solution—from initial assessment through production and optimization. Encora’s team will guide you in defining the right agents, orchestration, and governance to make this vision a reality.
References
The Alan Turing Institute. (n.d.). Multi-agent systems.
What are multi-agent systems? | SAP. (n.d.). https://www.sap.com/resources/what-are-multi-agent-systems#:~:text=A%20multi%2Dagent%20system%20is%20like%20a%20project%20manager%20or,can%20deliver%20a%20better%20performance
FCA calls for firms to improve bereavement handling times and shares best practice. (2024, November 22). FCA. https://www.fca.org.uk/news/press-releases/fca-calls-firms-improve-bereavement-handling-times-shares-best-practice
Seizing the AI advantage: Using the EU AI Act as a catalyst for innovation. (2024, October 11). Marc Hollyoak. Encora. https://www.encora.com/insights/seizing-the-ai-advantage-using-the-eu-ai-act-as-a-catalyst-for-innovation