Agentic AI in Insurance: Use Cases and Examples (2026)

Agentic AI in insurance uses AI agents to coordinate multi-step work, such as claim intake, evidence checks, policyholder updates, and underwriting preparation, within defined rules and escalation paths. The strongest early use cases are high-volume, bounded workflows where people approve consequential decisions and teams can audit every action.

Among 30 insurers tracked by Evident, 27 new AI use cases were announced in Q1 2026. Five were agentic and concentrated in claims and specialty underwriting. Only 18% of the new announcements reported an outcome, so agentic AI is best treated as an early operational deployment pattern, not proven sector-wide transformation. Evident, Q1 2026

What is Agentic AI in Insurance?

Agentic AI in insurance means moving a bounded task or decision into an agent that can interpret context, choose the next permitted step, use connected systems, document activity, and escalate exceptions. It does not mean handing over underwriting, claims settlement, or pricing decisions without controls.

The National Association of Insurance Commissioners (NAIC) defines an artificial intelligence system as a machine-based system that generates outputs such as predictions, recommendations, or content that influence decisions, and that can operate with varying levels of autonomy. Its model bulletin also states that insurer actions and decisions using AI systems must comply with applicable insurance laws and regulations. NAIC Model Bulletin, adopted December 4, 2023

The distinction matters because insurance teams should select an operating mode that matches the task’s consequence, variability, and required controls.

Operating mode	Appropriate use in insurance	Control to define
Human work	Complex coverage interpretation, disputes, sensitive customer situations, and consequential decisions	Decision owner, required evidence, and documented rationale
Automated rules	Explicitly defined checks, such as whether a required field is present	Rule owner, exception route, and change-control process
Agentic AI	Bounded, multi-step work that needs context, permitted tool use, and documented handoffs	Permitted actions, authoritative data sources, monitoring, and escalation rules

An agentic workflow therefore sits between a fixed script and unrestricted delegation. The agent may reason about the next step, but its available actions, data, messages, and escalation paths should be defined before deployment.

Where Agentic AI Fits in the Insurance Workflow

A recommended operating pattern for an insurance workflow is to capture the intake, retrieve policy or claim context, assess whether the case fits a defined path, take permitted actions, and hand off a complete record when the case exceeds the boundary.

The NAIC identifies AI uses across product development, marketing, sales and distribution, underwriting and pricing, policy servicing, claims management, and fraud detection. These are workflow categories, not a recommendation that every task within them belongs in agent mode. NAIC Model Bulletin

The table below shows where an agent may support work while leaving consequential judgment with an accountable professional.

Use case	What an agent can do	Keep people responsible for
First notice of loss and claim intake	Capture the customer narrative, retrieve policy data, identify missing facts, create a claim record, and route the case	Sensitive conversations, disputed facts, and complex coverage interpretation
Low-complexity claims coordination	Check predefined conditions, request documents, perform fraud-screening steps, and prepare a recommendation	Final settlement or denial decisions requiring review
Underwriting submission preparation	Extract fields, identify missing documents, compare inputs against underwriting guidance, and prepare a risk summary	Exceptions, novel exposures, and final underwriting judgment
Policyholder service	Answer grounded policy and claim-status questions, send reminders, and update customers on next steps	Advice beyond policy language, complaints, and escalated disputes
Fraud investigation support	Assemble records and flag contradictions for investigation	A fraud finding, adverse action, or other customer-impacting decision

For policyholder service, an agent can answer questions from approved policy and claim information, but it should not create advice beyond that information. Related AI chatbot use cases in insurance show the broader service category, while an agentic workflow adds permitted actions, system updates, and escalation rules.

Real Examples of Agentic AI in Insurance

The following are company-published accounts, not independent proof of outcomes. They show how insurers and insurance partners have scoped early deployments, but they do not establish universal claims, underwriting, or customer-service results. Allianz, Travelers, and McGill and Partners published the accounts.

Claims coordination

Allianz says Project Nemo went live in Australia in July 2025 for food-spoilage claims after severe weather. The workflow uses seven specialized agents for tasks including coverage checks, weather-event confirmation, fraud checks, payout calculation, and an audit summary.

Allianz states that a claims professional makes the final payment decision. That division is important: the agents coordinate evidence and prepare the case, while a person remains responsible for the consequential payment decision.

Auto-claim intake

On February 18, 2026, Travelers announced an agentic claim assistant for customers filing auto-damage claims. The assistant provides relevant policy information, answers claim questions, guides claim submission, and moves customers toward photo uploads, appraisals, repair scheduling, and rental-car steps.

Travelers also says customers can speak with a live specialist at any point. The example focuses on claim initiation and connected follow-up tasks rather than presenting the assistant as a replacement for claims professionals.

Specialty underwriting and capacity management

In March 2026, AIG and McGill and Partners announced a collaboration using agentic AI capabilities to manage capacity deployment. AIG said it created underwriting criteria intended to enable real-time underwriting through the broker’s digital platform.

This example concerns specialty underwriting and portfolio capacity rather than a general-purpose underwriting agent. The announcement describes a defined data and criteria layer supporting the workflow, which is different from delegating every underwriting judgment to an agent.

How to Choose a First Agentic AI Workflow

Start with a workflow that meets at least four of these six criteria:

High volume: The task occurs often enough to reveal repeatable patterns.
Bounded action set: The agent has a finite list of permissible actions, systems, and messages.
Authoritative context: Policy, claim, customer, and workflow data come from defined systems of record.
Clear escalation triggers: Missing evidence, conflicting data, regulatory thresholds, customer distress, and out-of-policy cases route to people.
Observable outcome: Teams can monitor completion, rework, handoffs, follow-ups, exceptions, and complaints.
Named process owner: Claims, underwriting, or service leadership owns the rules and approves changes.

This framework favors work that is repeatable without being purely mechanical. A task may need contextual interpretation and several system interactions, but the insurer should still be able to describe what the agent may do and when it must stop.

Hypothetical example: A carrier begins with storm-related food-loss claims below a locally approved value threshold. The agent collects the loss narrative, checks policy dates and coverage, compares the location with a verified weather event, requests missing evidence, and produces an audit summary. It cannot issue a final payment. A claims professional approves, changes, or rejects the recommendation.

A practical pilot could run for four to six weeks across a representative set of low-complexity cases. The carrier should check completion without rework, time to human handoff, missing-information follow-ups, escalation rate, override rate, and customer complaints.

The pilot should also record why cases leave the defined path. A high escalation rate may indicate that the workflow boundary is too narrow, the source data is incomplete, or the process has more variation than expected. Those findings are useful even when the carrier does not expand the deployment.

Governance Is Part of the Workflow, Not a Final Review Step

Requirements vary by product, state, and decision type. As of April 1, 2026, the NAIC implementation map listed 25 jurisdictions that had adopted its AI model bulletin, along with insurance-specific AI guidance in California, Colorado, New York, and Texas. The map notes that state action may not contain every element of the model bulletin. NAIC implementation map

New York provides one example of a state-specific approach, not a national rule. Its July 2024 circular letter says insurers using AI systems or external consumer data in underwriting and pricing should use a risk-based governance approach, assess potential proxy discrimination, oversee third parties, and remain responsible for outcomes. New York Department of Financial Services

Governance should be designed into the workflow before production use. An implementation checklist includes:

Permitted actions: Define which records the agent may read, create, change, or send.
Data sources: Identify authoritative policy, claim, customer, weather, and underwriting sources.
Human escalation rules: Specify triggers for missing evidence, conflicting records, complaints, distress, thresholds, and out-of-policy cases.
Audit trail: Record inputs, retrieved information, actions, messages, handoffs, recommendations, and human decisions.
Version control: Track changes to prompts, rules, connected tools, data mappings, and workflow instructions.
Testing against historical cases: Replay representative cases, including exceptions and cases that previously required rework.
Complaint monitoring: Review complaints and adverse customer experiences for patterns linked to the workflow.
Rollback process: Keep a tested way to return to the prior workflow version if a change creates unacceptable results.

For underwriting and pricing, governance also needs clear ownership across underwriting, compliance, legal, data, technology, and operations. A vendor may provide a system, but the insurer remains responsible for deciding whether the workflow fits its obligations and risk tolerance.

Applying the Framework to an Insurance Deployment

The safest starting point is not the most visible use case. It is the process where the insurer can define the boundary, identify the source data, observe the result, and assign a decision owner.

Claims intake, document follow-up, policy-status questions, underwriting submission preparation, and low-complexity claims coordination may fit that pattern. Final claim decisions, disputed coverage, novel exposures, adverse actions, and sensitive customer situations require stronger human involvement and may not be appropriate for an initial agentic deployment.

NuPlay AI runs enterprise workflows in production and improves them after every run through NuLoop, with agents, the systems they operate, and the context they draw on under one platform. AI agents for insurance can support defined insurance workflows, while NuLoop proposes scoped changes, tests them against real past runs, and a person approves every change before it ships through approve-to-promote governance. NuLoop works through five stages: Report, Diagnose, Propose, Try, Ship. A person approves every change before it ships.

NuPlay AI's published First Mid Insurance case study reports a 25% increase in team productivity; this is a vendor-published claim, not independent verification. NuPlay AI- customer success stories

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