(Executive Summary: Most insurers spent 2023 experimenting with generative AI—proofs of concept, narrow pilots, chatbot trials. A year later, the bar has shifted. Stakeholders aren’t asking, “Can AI write a claims summary?” They’re asking, “Can AI actually move the needle on how we operate?”
This whitepaper focuses on a concrete answer: multi-agent systems.
We’ll explore how agentic AI (systems composed of task-specific, autonomous agents) are being used to rewire insurance operations from within. We’ll focus on property and casualty (P&C) insurers, where the transactional nature of core functions makes them a natural testbed. This paper avoids the hype and gets into structure, roles, friction points, and what it takes to get real business value.)
Autonomy, not just intelligence, is what changes the game
In 2023, GenAI showed that language models could mimic understanding, draft human-like text, and assist in isolated tasks. Useful, but narrow. In 2024-25, a more operationally significant evolution has gained ground: agentic AI. Systems composed of multiple, goal-oriented agents that:
- Observe and respond to real-world data
- Initiate and sequence tasks without being explicitly prompted
- Collaborate with other agents to complete processes
- Learn from past results and adapt behavior
This is not a linear “next step” in the GenAI story. It’s a categorical shift from single-tool intelligence to distributed, interactive systems. More importantly, it pushes organizations to stop thinking about AI as a helper that sits beside a human and start thinking about AI as a contributor that owns tasks, takes initiative, and operates with accountability. How exactly can insurers benefit from this revolution?
From underwriting to fraud detection, the deployment curve has started.
In insurance, a task is rarely standalone. Underwriting a policy or adjudicating a claim requires sequencing actions, fetching data from multiple systems, making conditional decisions, and communicating outcomes. This is where single-shot LLMs hit a wall and where agents begin to shine.
Let’s break it down function by function.
Underwriting
Underwriting is both data-heavy and judgment-based. It’s ripe for agent decomposition:
- Intake Agents parse application forms, extract structured data, and cross-check for completeness.
- Risk Assessment Agents pull from actuarial models, internal risk scores, and third-party data sources.
- Eligibility Agents apply business rules and recommend acceptance, flagging edge cases.
Swiss Re reports a 75% modular underwriting automation. and notes that agents can ingest broker submissions, auto-triage by risk, and request missing documentation autonomously. McKinsey forecasts that by 2030, a majority of underwriting work in P&C may be handled this way.
Claims Adjudication
This process spans intake, verification, investigation, and resolution. Agents can act as checkpoints:
- FNOL Agents standardize incoming reports from email, app, or call center.
- Validation Agents cross-check with policy terms, customer records, and previous claims.
- Assessment Agents calculate liabilities, considering deductible rules and damage tables.
Cognizant adds that agentic systems now handle even edge-case claims at near-human accuracy.
Fraud Detection
AI fraud tools are nothing new. What’s new is an architecture that lets fraud agents behave more like an intelligence unit:
- Surveillance Agents flag anomalies in real-time, across regions and channels.
- Context Agents dive deeper into flagged items, comparing them against known fraud patterns.
- Escalation Agents assign severity and determine whether to alert a human investigator.
The Coalition Against Insurance Fraud estimates $300B+ is lost annually. Agentic systems offer continuous pattern recognition, faster escalation, and ongoing learning.
Customer Engagement
Multi-agent setups enable more intelligent, sustained engagement:
- Onboarding Agents handle setup and education when a customer buys a policy.
- Service Agents handle inbound queries and personalize outbound alerts.
- Sentiment Agents monitor tone across interactions and adjust accordingly.
Allstate has adopted AI to draft over 50,000 customer emails per day, using tone-aware models that communicate more empathetically than human reps. Meanwhile, Zurich Insurance created a CRM engine that behaves more like Spotify, serving agents with predictive insights within three clicks and cutting servicing times by 70%.
The hardest part isn’t the AI.
It’s everything around it that wasn’t built for agents.
Implementing agentic AI isn’t a matter of turning on a platform. It requires a shift in architecture, oversight, and human-machine interaction. The most common barriers include:
- Process Complexity
Insurance processes are long-tail and context-sensitive. An agent can’t just “replace” a process—it needs to be embedded within workflows that are often messy and undocumented.
- Legacy Integration
Policy admin and claims systems weren’t built for AI interoperability. Orchestrating agents across platforms like Guidewire or Duck Creek means building robust API middleware, not quick scripts.
- Coordination Overhead
The more agents you have, the harder it gets to manage task sequencing and dependency tracking. This is the orchestration problem and it’s the linchpin of any agent deployment.
- Governance and Explainability
In highly regulated industries, agents can’t just act—they need to justify. You need audit trails, override mechanisms, and documentation to explain decisions to regulators and auditors.
- Observability
Without visibility into what agents are doing, you lose trust. You need dashboards, logs, performance analytics. This isn’t just MLOps 2.0—it’s AgentOps.
- Human-AI Co-working Models
Agents that do too little are useless. Agents that do too much without human alignment are dangerous. Designing the right level of human-in-the-loop is a challenge that can’t be templated.
- Cultural Resistance
Change is not just technological. Employees, especially in high-context decision roles, may resist systems that appear to encroach on their expertise. Internal comms and change design need to lead the adoption, not follow it.
Operational success with agentic AI is less about the model, more about the method
Here’s what successful insurance teams are doing to turn multi-agent talk into actual operational value.
Start Small and Concrete
Pick one domain—like subrogation, FNOL, or policy endorsements—where the task is repetitive and rule-bound. Design 3–5 agents to take it end-to-end. Measure outcomes rigorously.
- Design Agents Like Microservices
Avoid the trap of trying to build a single “AI brain.” Each agent should have a narrowly defined job, a clear interface, and be replaceable.
- Invest in Orchestration Infrastructure
You’ll need something that acts like an operating system for agents. Open-source tools like LangGraph, AutoGen, and CrewAI are early bets; some insurers are building in-house. Either way, orchestration is not optional.
- Embed Governance at the Start
Don’t build first and bolt on explainability later. Design agents with explainable steps and data lineage tracking from day one.
- Create AgentOps as a Function
Monitoring, tuning, and managing your agent workforce requires a new function. Just like DevOps or MLOps, AgentOps needs its own tools, KPIs, and processes.
- Upskill the Human Side
Introduce roles like Agent Designers, Agent Managers, and AI Process Analysts. Train existing teams to collaborate with agents—not compete with them.
- Use Metrics That Reflect Real Change
Skip vanity metrics. Good agent implementations show measurable improvements in claim cycle times, underwriting throughput, customer response times, and fraud detection rates. Define those KPIs before launch, and track them obsessively.
- Reinforce with Scenario-Based Testing
Before deployment, run agents through real claim examples, edge cases, compliance checks. Let them fail in a safe sandbox. It’s not enough for an agent to function—you need to know how it fails, recovers, and adapts.
Agentic AI rewards execution, not exploration
Multi-agent systems are the next operating model for digital insurance. They don’t just answer questions—they get things done. But success doesn’t come from buzzwords. It comes from treating agents as employees: with onboarding, supervision, escalation rules, and metrics. It comes from avoiding overreach and building trustable components that fit the processes insurers actually run.
It’s just the next layer of serious, system-aware AI.
And for insurers who move first and implement well, it’s an edge that compounds.
The PalTech Way:
At PalTech, we believe the insurance industry has moved beyond AI experimentation. The real opportunity lies in operationalizing agentic AI—systems that think, act, and deliver measurable business outcomes.
While many are still talking about what’s next, we’ve already built what’s now. PalTech has deployed agentic AI solutions in production, empowering insurers to unlock new levels of efficiency, decision intelligence, and customer engagement.
This isn’t a 1–2 year horizon—it’s happening today. If you’re ready to move from pilots to performance, let’s talk.
Appendix: Emerging Tools and Platforms
- Orchestration & Coordination: CrewAI, LangGraph, AutoGen
- Monitoring & Observability: Arize, WhyLabs, TruEra
- Frameworks for Agent Design: ReAct, CAMEL, OpenAgents
- Enterprise Integration: Azure AI Studio, AWS Bedrock, Google Vertex AI