Collaboration is the backbone of the insurance industry — from underwriters and actuaries to claims adjusters and customer service teams. As artificial intelligence (AI) capabilities expand, multi-agent systems (MAS) are emerging as a scalable way to replicate and enhance these interactions.

Rather than relying on a single model, MAS bring together multiple AI agents, each designed to handle a specific task, to work in coordinated, intelligent workflows. How is a MAS different from an agentic system? MAS are often decentralized, meaning no single agent necessarily “owns” the workflow. By enabling AI-driven agents to work in tandem, insurers can deliver more personalized experiences, reduce costs, and ensure compliance with evolving regulations.

MAS can power everything from underwriting and fraud detection to customer engagement and compliance. Sharing intelligence and automating routine decisions enables these agents not only to reduce manual workloads but also to respond in real time to regulatory shifts and evolving market demands — making MAS a scalable, future-ready solution for insurers.

Challenges to Implementing Multi-Agent Systems

While the insurance use cases for MAS are compelling, unlocking these benefits at scale isn’t as simple as plugging in a new system. MAS implementation introduces a new layer of complexity, requiring insurers to rethink not only their technology stack but also their governance, data practices, and organizational readiness.

To start with, the AI agents must work in sync to avoid conflicting decisions. Without effective communication protocols, automation can become inefficient or even contradictory. Coordination frameworks like contract-net or publish-subscribe systems help ensure agents operate cohesively.

As agents share and process sensitive policyholder data, maintaining strict privacy and security standards is essential. Approaches like privacy-by-design and federated learning can help protect data while supporting accurate, compliant decision-making. MAS decisions must remain explainable, transparent, and fair to meet evolving regulatory demands.

On top of these coordination, security, and regulatory concerns, many insurers still operate on aging core platforms that don’t easily support AI integration. Modernizing infrastructure or using cloud-native architectures is key to unlocking MAS capabilities without disrupting existing operations. Addressing these foundational challenges with the right strategy and infrastructure will better position insurers to scale their MAS efforts and to prepare for what’s ahead as these systems become even more intelligent and integral to the industry’s future.

How Multi-Agent Systems Can Transform P&C Insurance

Once an insurer has navigated the challenges to implementing MAS, there are a variety of ways a carrier can use the technology to streamline complex processes and respond more effectively to the needs of their policyholders. In fact, some insurers have already seen results from leveraging MAS across the value chain.

1. Intelligent Underwriting

Underwriting in P&C insurance involves assessing risks by analyzing diverse data sources, including historical claims, weather patterns, and customer demographics. Implementing MAS can significantly enhance this process by automating and integrating various functions. Multi-agent AI in underwriting could look like:

• Underwriting Agent: Evaluates applications against predefined risk models.​
• Fraud Detection Agent: Identifies inconsistencies in customer data.​
• Regulatory Compliance Agent: Ensures decisions adhere to current regulations.​

For instance, Progressive uses AI-driven algorithms to analyze data points more rapidly and accurately than traditional manual underwriting methods. This approach enables faster risk assessment and more precise offers to customers, enhancing overall satisfaction as a result. ​

2. Automated Claims Processing

Traditional claims processing often involves extensive manual intervention, leading to delays and increased operational costs. MAS can automate and optimize this workflow through:​

• Claims Assessment Agent: Reviews claim details, including photos and reports.​
• Fraud Detection Agent: Cross-references claims against known fraudulent patterns.​
• Customer Service Agent: Keeps policyholders informed about their claim status.​
• Legal & Compliance Agent: Ensures all actions comply with regulatory standards.​

After Aviva France implemented intelligent automation, the insurer saw dramatic improvements: Same-day claim settlements rose from 1% to 25%, and claims processed within three days increased by 530%. These gains demonstrate the power of coordinated, intelligent systems to enhance both efficiency and customer satisfaction.

3. Dynamic Customer Engagement

Policyholders expect real-time, personalized interactions with their insurers. MAS facilitates this by leveraging a variety of AI agents:

• Customer Service Agent: Provides instant responses via chatbots and email automation.​
• Underwriting Agent: Suggests coverage adjustments based on lifestyle changes.​
• Claims Agent: Guides customers through the claims filing and tracking process.​

By integrating MAS into customer engagement channels, insurers can offer 24/7 service, leading to improved retention and more meaningful interactions.

4. Regulatory Compliance & Risk Management

Navigating the complex and evolving landscape of insurance regulations requires constant vigilance. MAS can assist in maintaining compliance through:​

• Regulatory Compliance Agent: Updates underwriting and claims rules based on new regulations.​
• Risk Management Agent: Assesses emerging risks and recommends policy adjustments.​
• Audit Agent: Tracks decisions made by other agents to ensure transparency.​

Automating compliance tasks with MAS reduces regulatory risks, helps avoid penalties, and supports ethical business practices.

By adopting multi-agent AI, P&C insurers can transform their operations, achieving greater efficiency, accuracy, and customer satisfaction across various their entire business.

Powering the Future of Insurance With Multi-Agent Systems

As insurers seek smarter, more scalable solutions, MAS are emerging as a key enabler of AI-powered collaboration across the P&C value chain. From underwriting and claims to compliance and customer engagement, these intelligent agents collaborate to reduce costs, enhance decision-making, and improve the policyholder experience.

As AI continues to evolve, the potential applications of MAS will only expand. Future innovations may include decentralized insurance agents managing smart contracts via blockchain, real-time risk assessment agents adjusting premiums using IoT and telematics data, and autonomous claims adjusters leveraging computer vision to assess damages from photos and videos.

For insurers, MAS are more than a tool for automation — they are the foundation of a truly intelligent insurance ecosystem. The question is no longer whether to adopt MAS, but how quickly insurers can scale them to stay competitive in an increasingly digital, data-driven market.

If you’re interested in learning more about the future of AI in insurance, read my e-book, “The History of AI in Insurance and Where It’s Headed.”