Using LangChain and Arcade.dev to Build AI Agents For Insurance (P&C/Life): Top 3 Use Cases

Key Takeaways

• Insurance AI agents unlock $60-80 million in cost savings but fail at multi-user authorization: Property & casualty insurers report $60-80 million annually in cost savings from AI automation, yet agents stall at production because they can't securely act on behalf of thousands of independent agents, claims adjusters, and underwriters — Arcade.dev's MCP runtime solves this authorization gap
• LangChain orchestrates workflows while Arcade enables secure action: LangChain provides the reasoning framework for multi-step insurance workflows, but without Arcade's delegated user authorization and scoped permissions, agents can't safely access carrier portals, policy management systems, and customer communication platforms
• Claims processing delivers fastest ROI: Insurers implementing AI agents for claims automation achieve 50-70% reduction in manual processing time and 30% improvement in routing accuracy, with approval times dropping from days to hours
• Independent agent management represents the largest authorization challenge: Managing thousands of independent agents accessing multiple carrier portals requires per-agent, per-carrier OAuth credentials without exposing tokens to LLMs — a problem traditional platforms can't solve at scale
• 75% of health insurers use AI for customer service but security barriers block broader production: 75% of health insurers leverage AI for customer service, yet most remain in pilot phase because performance quality concerns and authorization complexity prevent domain-wide deployment

Here's what most insurance companies get wrong about AI agents: they build impressive proof-of-concepts demonstrating AI-powered claims triage or policy recommendations, then hit an authorization wall when deploying to production. The gap isn't technical capability — it's the unsolved problem of letting AI agents securely act on behalf of multiple users across fragmented, domain-specific enterprise systems.

Arcade.dev's AI tool-calling platform closes this gap by serving as the MCP (Model Context Protocol) runtime that enables and governs multi-user authorization across tools. When your LangChain agent needs to read claims data from policy management systems, send status updates via Gmail, coordinate with adjusters through Slack, and access multiple carrier portals — Arcade handles the delegated user authorization and scoped permissions that make these actions safe, auditable, and compliant.

Arcade’s MCP runtime does not handle your insurance data directly; it focuses on token and secret management so agents can call these systems safely without exposing underlying credentials to the language model.

The market opportunity is substantial. Insurers implementing AI agents report cost savings of $60-80 million annually, with 30-60% reduction in manual processing time. Yet performance quality concerns and authorization complexity keep most implementations in pilot phase. For insurance companies that solve multi-user authorization first, the competitive advantage compounds: faster claims processing, improved underwriting accuracy, and enhanced customer engagement.

Building these agents without Arcade means assembling custom OAuth flows for every carrier portal and policy system, managing token lifecycle and refresh logic across thousands of independent agents, implementing fine-grained permission scoping for different user roles, maintaining audit trails for regulatory compliance, and handling edge cases when user access changes — multiplied across dozens of enterprise platforms. Teams attempting this path typically spend 6-12 months on multi-user authorization infrastructure before writing their first production agent workflow.

What Are AI Agents and Why Insurance Companies Need Them Now

AI agents differ fundamentally from chatbots: chatbots respond to queries, while agents take autonomous actions on behalf of users. In insurance contexts, this means an agent doesn't just answer "what's the status of claim #12345?" — it reads your claims management system, checks adjuster calendars, identifies processing bottlenecks, and sends coordination messages to relevant stakeholders.

This distinction matters because insurance operations run through manual, error-prone workflows distributed across fragmented systems. Claims adjusters toggle between policy databases, communication platforms, carrier portals, damage assessment tools, and payment systems. Independent agents navigate multiple carrier platforms to quote policies, track commissions, and manage multi-carrier client portfolios. Underwriters coordinate across medical records, credit reports, risk assessment models, and approval workflows.

AI agents collapse these fragmented workflows into conversational interfaces backed by authenticated tool access. The business case is compelling: insurers report 80% of customer onboarding automated, 23-day reduction in liability assessment for complex cases, and 50% reduction in claims processing time.

But deployment requires solving multi-user authorization at scale. When an AI agent acts, it needs:

• Delegated user permissions — not system-level admin access
• Scoped tool access — reading claims data doesn't grant permission to approve payments
• Just-in-time authorization — users approve sensitive actions before execution
• Audit trails — every agent action tracked for regulatory compliance
• Token security — credentials never exposed to the LLM itself

Traditional chatbots avoid these requirements by staying read-only and advisory. Production insurance agents require write access to mission-critical systems — which is why multi-user authorization becomes the primary barrier blocking deployment.

Why Multi-User Authorization Matters in Insurance AI Deployments

Insurance companies operate in highly regulated environments where data breaches, unauthorized access, and compliance failures carry devastating consequences. State insurance departments require bulletproof audit trails and data protection. NAIC model laws impose strict privacy requirements. SOC 2 audits validate security controls.

The authorization challenge compounds when agents need to act across multiple user contexts. An independent agent management platform serving 1,000 agents across 20 carrier systems requires secure access to each agent's carrier credentials — without storing persistent tokens or granting blanket system access.

Building this infrastructure from scratch forces insurance development teams into problems outside their core expertise:

• Implementing OAuth 2.1 flows for Gmail, Slack, Salesforce, and custom carrier portals
• Managing token refresh, expiration, and revocation across thousands of users
• Scoping permissions so agents access only what each user has authorized
• Maintaining compliance documentation for every authorization pattern
• Handling edge cases when agents leave agencies or revoke access

Teams attempting custom multi-user authorization solutions typically burn 6-12 months before shipping their first production agent — time competitors using Arcade's MCP-compatible platform spend refining agent intelligence and business value.

How LangChain and Arcade.dev Work Together to Enable Secure Insurance AI Agents

LangChain has emerged as the leading framework for building AI agents, with broad adoption across insurance companies. The framework excels at chaining LLM-driven tasks, managing retrieval workflows, and orchestrating multi-step agent reasoning. LangGraph — the graph-based orchestration and state management layer built on top of LangChain — introduces conditional logic and decision points that let agents handle complex workflows like claims coordination or multi-carrier policy comparison.

Arcade.dev serves as the MCP runtime that enables and governs multi-user authorization across tools, integrating with LangChain for secure tool execution. While LangChain handles agent orchestration and reasoning, Arcade manages the critical infrastructure that lets agents safely interact with real-world systems:

LangChain's Role: Agent Orchestration and Reasoning

• Chains LLM calls for multi-step task decomposition
• Manages agent state and conversation context
• Routes decisions through conditional logic graphs
• Coordinates multi-agent collaboration workflows

Arcade's Role: MCP Runtime for Secure Tool Execution and Multi-User Authorization

• Provides OAuth-based access to hundreds of enterprise platforms — including Gmail, Slack, carrier portals, and policy systems — via Arcade’s tool catalog
• Manages delegated user tokens and secrets without exposing them to LLMs
• Enforces scoped permissions and just-in-time approval workflows
• Maintains audit trails for regulatory compliance
• Handles token lifecycle, refresh, and revocation

This separation of concerns lets insurance teams focus on agent intelligence rather than authorization infrastructure. Instead of building custom OAuth flows for every carrier portal and policy system, organizations use Arcade's tool catalog of pre-built MCP servers for common platforms and its MCP framework for custom tools, even when they are not in the shared catalog. AI/ML teams get reusable building blocks for workflows, security teams get centralized multi-user authorization and audit controls, and business leaders get production agents that can safely take real actions in core insurance systems.

LangChain's Role: Orchestrating Complex Insurance Workflows

LangChain enables insurance AI agents to decompose complex tasks into manageable steps, maintain context across multi-turn conversations, and coordinate specialized sub-agents for different workflow components.

For a property & casualty claims processing agent, LangChain might orchestrate:

1. First Notice of Loss (FNOL) intake and data extraction from emails or phone transcripts
2. Policy verification and coverage determination across multiple systems
3. Damage assessment coordination with photo analysis and adjuster scheduling
4. Fraud detection checks against historical patterns and third-party databases
5. Settlement recommendation generation based on policy terms and precedent
6. Communication automation for status updates to all stakeholders

LangGraph extends this with conditional decision points. When the agent encounters ambiguous coverage situations, it routes to human underwriting review. When it identifies clear liability and straightforward damages, it proceeds automatically to settlement recommendation. This visual flow control with conditionals makes insurance workflows more transparent and auditable — critical for regulatory validation.

The framework's strength is orchestration, not authorization. LangChain assumes tools are already accessible and correctly scoped. This works for internal demos but fails in production multi-user environments where different adjusters, agents, and underwriters need different access levels to the same carrier systems.

Arcade's Role: Solving the Multi-User Authorization Problem

Arcade solves the authorization gap by serving as the MCP runtime between LangChain agents and the tools they need to access. When an insurance AI agent calls a tool, Arcade:

1. Validates user consent and permissions: Confirms the user has granted the agent permission to access this specific tool
2. Retrieves scoped credentials: Fetches the user's OAuth token with appropriate permission boundaries
3. Executes the tool call: Runs the action (send email, query policy system, access carrier portal) on behalf of the user
4. Logs the action: Maintains immutable audit trail for compliance requirements
5. Returns results: Sends tool output back to LangChain agent without exposing credentials

Arcade manages tokens and secrets rather than raw claims or policy data, so sensitive insurance information stays within your existing systems while agents still gain controlled access.

For insurance deployments, Arcade’s MCP runtime can be deployed within existing security and compliance boundaries while maintaining the same tool catalog and multi-user authorization patterns.

Why MCP Compatibility Matters for Insurance Enterprise Deployments

The Model Context Protocol (MCP) standardizes how AI agents access tools and data sources. Arcade's native MCP support means insurance teams can:

• Connect to any MCP server over HTTP transport
• Use tools from the broader MCP ecosystem
• Build custom MCP servers for proprietary policy systems
• Maintain compatibility as LangChain and other frameworks adopt MCP

This matters because insurance AI infrastructure is fragmented and domain-specific. A claims processing agent might need access to:

• Commercial SaaS platforms (Salesforce, Microsoft Dynamics, Guidewire)
• Internal policy management systems and claims databases
• Cloud storage (Google Drive, SharePoint) for documentation
• Communication tools (Gmail, Slack, Microsoft Teams)
• Multiple carrier portals with different authentication mechanisms

MCP compatibility means these tools work together through a common protocol rather than requiring custom integration code for each system. Insurance teams can add new carrier portals or policy systems to their agent workflows without rebuilding authorization infrastructure.

Use Case 1: Streamlining P&C Claims Processing with AI Agents

Property & casualty claims processing represents the highest-value automation opportunity for insurance AI agents. Manual claims workflows consume adjuster time on administrative coordination rather than judgment-based assessment. Customers wait days for status updates. Fraud detection depends on individual adjuster experience. Settlement consistency varies across adjusters and regions.

The claims workflow fragments across multiple systems and stakeholders:

• First Notice of Loss (FNOL): Data entry from phone calls, emails, or web forms
• Policy verification: Checking coverage, deductibles, limits, and exclusions
• Damage assessment: Coordinating inspections, analyzing photos, estimating repair costs
• Fraud detection: Cross-referencing claims history, identifying suspicious patterns
• Settlement negotiation: Calculating appropriate payment based on policy terms and damages
• Communication: Status updates to policyholders, agents, repair shops, and other parties

AI agents automate significant portions of these workflows while maintaining human oversight for complex judgment calls. The business impact is measurable: 30% improvement in claims routing accuracy, 50-70% reduction in manual processing time, and claims approval moving from days to hours.

The agent workflow operates like this:

• Automated FNOL intake: Extracting structured data from emails, voice transcripts, or forms
• Multi-system policy verification: Checking coverage across policy databases and carrier systems
• Intelligent damage assessment: Analyzing photos with computer vision, scheduling adjuster visits, coordinating repair estimates
• Proactive fraud detection: Cross-referencing claims patterns, vehicle histories, medical records
• Settlement recommendation: Calculating appropriate payment within policy limits and precedent
• Stakeholder communication: Automated status updates via email, SMS, and portal notifications

Building this without Arcade means implementing authorization separately for each claims system, carrier portal, and communication platform. The policy management database requires OAuth flows. Multiple carrier portals each need separate credential management. Gmail and Slack access for communication requires Google and Slack authentication. Each integration consumes development time and creates maintenance burden.

Automating First Notice of Loss (FNOL) with Multi-System Access

FNOL processing exemplifies the authorization complexity insurance agents must solve. When a policyholder reports a claim via email, the agent needs to:

• Read the email: Requiring Gmail API access with the adjuster's credentials
• Extract claim details: Parsing date, location, description, involved parties
• Verify policy coverage: Checking internal policy database for active coverage
• Check carrier systems: Confirming coverage details across potentially multiple carriers for commercial policies
• Create claim record: Writing to claims management system with appropriate claim number
• Send acknowledgment: Emailing policyholder confirmation with claim number and next steps
• Notify stakeholders: Alerting assigned adjuster and agency via Slack or email

Each of these actions requires authenticated access to different systems. The agent operates with the adjuster's permissions, not system-level admin access. When Adjuster A processes a claim, the agent accesses only policies and systems Adjuster A is authorized to view. When Adjuster B processes a different claim, the same agent operates within Adjuster B's permission boundaries.

Traditional approaches grant the AI application database credentials and email accounts, creating security risks and audit trail gaps. When an agent has system-level access, any user can potentially access any claim — violating need-to-know principles and creating compliance exposures.

Arcade's multi-user authorization model enforces user-specific permissions through delegated OAuth tokens. The agent inherits each adjuster's existing access rights across all systems, maintaining the same security posture as human users while enabling automation.

Accelerating Settlements with Intelligent Damage Assessment

Property damage assessment requires coordinating multiple data sources: policyholder photos, adjuster inspections, repair estimates, historical claims data, and fraud indicators. AI agents can automate significant portions of this workflow while escalating complex cases to human adjusters.

For straightforward claims — minor vehicle damage with clear liability, standard home repairs from covered perils — agents can:

• Analyze submitted photos using computer vision for damage assessment
• Cross-reference repair costs against regional pricing databases
• Check claims history for similar incidents and fraud patterns
• Calculate settlement within policy limits and deductible
• Generate settlement offer with supporting documentation
• Route for adjuster approval based on confidence thresholds

For complex claims — disputed liability, unusual damages, high-value losses — agents surface relevant information and route to specialized adjusters rather than attempting autonomous settlement.

This hybrid approach requires sophisticated authorization: agents need read access to analyze data and make recommendations, but write access for settlements should require human approval based on dollar thresholds and risk scores. Arcade's just-in-time authorization enables these approval workflows — agents can propose actions that trigger user confirmation before execution.

Use Case 2: Enhancing Life Insurance Underwriting with AI Agents

Life insurance underwriting combines data analysis, risk assessment, and policy pricing decisions. Traditional underwriting workflows involve manual review of medical records, prescription histories, motor vehicle reports, credit checks, and applicant interviews. Processing times stretch weeks or months. Underwriter decisions vary based on individual judgment and experience.

AI agents transform underwriting through:

• Comprehensive data ingestion: Pulling medical records, prescription databases, MIB reports, MVR data, credit scores
• Automated risk assessment: Analyzing health indicators, lifestyle factors, family history against actuarial models
• Predictive modeling: Calculating mortality risk and appropriate premium pricing
• Policy recommendation: Generating coverage amounts, premium rates, and policy terms
• Compliance validation: Checking state regulations, required disclosures, and underwriting guidelines
• Application processing: Managing document collection, signature workflows, and policy issuance

The business value is clear: 23-day reduction in liability assessment for complex cases, faster time-to-issue for applicants, and consistent underwriting decisions across different underwriters and regions.

The challenge is authorization across fragmented data sources. Medical records require HIPAA-compliant access. Prescription histories come through pharmacy benefit databases. Motor vehicle reports access state DMV systems. Credit reports require FCRA-compliant authorization. Each data source has different authentication mechanisms and privacy requirements.

Without Arcade, underwriting teams build custom integrations for each data source, implement separate authorization flows, manage credential storage and refresh logic, and maintain audit trails for compliance. This infrastructure work delays agent deployment by months while consuming development resources.

Automated Risk Analysis Across Multiple Data Sources

Underwriting risk assessment requires synthesizing data from sources with different authorization requirements:

• Medical records: HIPAA-protected health information requiring patient authorization and provider authentication
• Prescription databases: Controlled substance monitoring program (CSMP) access with state-specific requirements
• Motor vehicle reports: State DMV systems with driver privacy protections
• Credit reports: FCRA-compliant access through credit bureaus
• MIB Group reports: Insurance industry data sharing with strict usage guidelines

Each source requires not just technical integration but appropriate legal authorization and audit documentation. An underwriting agent operating on behalf of Underwriter A needs access scoped to applications assigned to Underwriter A, not all applications in the system.

Arcade's delegated authorization model maintains these permission boundaries while enabling agents to act across all necessary data sources. The agent requests applicant consent for data access, validates authorization, retrieves information from each source, and maintains complete audit trails for regulatory review.

For insurers, this means underwriting agents can achieve comprehensive risk assessment without compromising data privacy, regulatory compliance, or security controls.

Accelerating Application Processing with Just-in-Time Authorization

Life insurance applications involve extensive document collection: medical exam results, physician statements, financial documentation, beneficiary information, and state-specific disclosure forms. Coordinating document collection across applicants, medical providers, and financial institutions creates delays and administrative burden.

AI agents can automate document coordination through email integration and calendar scheduling:

• Sending document requests via email with tracking
• Scheduling medical exams through calendar integration
• Following up on missing documents automatically
• Validating document completeness and accuracy
• Routing complete applications to underwriters for final review

These workflows require authenticated access to each underwriter's email and calendar, maintaining appropriate permission scoping so agents can only access applications within their assigned workload.

Arcade's MCP runtime for multi-user authorization makes this straightforward — underwriters authorize the agent to access their Gmail and Google Calendar, and the agent operates within those permissions without requiring system-level email access that would expose all communications.

Use Case 3: Personalized Customer Service and Engagement in Insurance

Insurance customer service operates through fragmented touchpoints: phone calls, emails, web portals, mobile apps, and agent interactions. Customers wait on hold, get transferred between departments, repeat information, and experience inconsistent service quality. Agents spend time answering routine policy questions rather than advising on coverage needs or selling additional products.

AI agents transform customer engagement by:

• 24/7 availability: Answering policy questions, processing routine requests, and scheduling agent callbacks
• Proactive communication: Renewal reminders, coverage gap notifications, claims status updates
• Personalized recommendations: Identifying upsell opportunities based on life events and coverage analysis
• Seamless handover: Escalating complex issues to human agents with full context
• Multi-channel consistency: Maintaining conversation state across email, chat, phone, and portal interactions

The business impact includes 36-point increase in customer satisfaction scores, 10-15% increase in premium growth through better retention and cross-sell, and significant reduction in contact center costs.

The authorization challenge is maintaining personalized access across customer accounts and communication channels. An agent serving customer interactions needs access to that customer's policy information, claims history, billing records, and communication preferences — but only for the duration of that customer conversation.

Building Multi-Turn Conversational Agents with Secure Policy Access

Agentic chat for insurance requires maintaining conversation context while securely accessing customer data. When a policyholder asks "what's my deductible for windshield replacement?", the agent:

• Validates customer identity through authentication
• Retrieves the customer's auto policy from the policy management system
• Extracts glass coverage details and deductible amount
• Explains coverage in plain language
• Offers to connect with glass repair shops or file a claim

This multi-turn conversation requires persistent access to customer data throughout the session, but that access should terminate when the conversation ends. Traditional systems either maintain persistent database connections (creating security risks) or require re-authentication for each query (degrading user experience).

Arcade's session-based authorization provides appropriate security boundaries — customers authenticate once at conversation start, the agent maintains scoped access to their policy data during the session, and credentials are revoked when the session ends.

For insurance companies deploying customer service agents across thousands of concurrent conversations, this authorization model scales without requiring custom session management infrastructure.

Proactive Outreach for Renewals and Coverage Optimization

Insurance agents spend significant time on renewal calls, coverage reviews, and upsell conversations. AI agents can automate proactive outreach while escalating qualified opportunities to human agents:

• Renewal reminders: Automated emails 60 and 30 days before policy expiration
• Coverage gap analysis: Identifying underinsured properties or life changes requiring coverage updates
• Competitive pricing alerts: Notifying when policy reviews might yield savings
• Life event triggers: Reaching out when public records indicate home purchase, marriage, new vehicle registration
• Claim-free discounts: Proactively applying eligible discounts and notifying policyholders

These workflows require Slack integration for agent coordination, email automation for customer outreach, and access to policy systems for coverage analysis. The agent identifies opportunities, drafts communications, and routes high-value prospects to human agents with full context.

For independent agencies managing thousands of policyholders across multiple carriers, this automation represents massive productivity gains — agents focus on complex advice and relationship building while AI handles routine communications and opportunity identification.

Solving the Independent Agent Authorization Challenge

Independent insurance agents represent the most complex multi-user authorization scenario in the industry. Each agent accesses multiple carrier portals to quote policies, bind coverage, track commissions, and manage client relationships. A mid-sized independent agency with 50 agents might interact with 20+ carrier systems, each requiring separate credentials, session management, and permission handling.

The traditional workflow forces agents to:

• Log into multiple carrier portals daily (average 8-12 different systems)
• Remember dozens of username/password combinations or manage them through password managers
• Navigate different user interfaces, data formats, and workflow patterns for each carrier
• Manually aggregate commission reports, policy listings, and client data across systems
• Re-authenticate frequently due to carrier security policies and session timeouts

AI agents could eliminate this friction by acting as a unified interface across all carrier systems — but only if they can securely manage per-agent, per-carrier credentials without exposing tokens to LLMs or creating compliance vulnerabilities.

Building this authorization infrastructure from scratch requires:

• OAuth integration with 20+ carrier platforms (each with unique implementation quirks)
• Per-agent, per-carrier token storage with encryption at rest
• Token refresh logic handling expiration and revocation across all carriers
• Session management respecting carrier security policies
• Audit trails documenting every carrier portal access and action
• Compliance documentation for agency E&O insurance and carrier agreements

Insurance technology vendors attempting this typically spend 12-18 months building authorization infrastructure before delivering agent-facing features. Agencies implementing custom solutions face the same timeline plus ongoing maintenance burden as carriers change authentication requirements.

Arcade's MCP runtime and tool catalog abstract this complexity — agencies configure carrier OAuth connections once, agents authorize access to their carrier credentials, and AI agents can then execute queries like "pull my commission reports from all carriers for Q4" without agents manually logging into each system.

Managing Thousands of Per-Agent, Per-Carrier Credentials

The independent agent authorization problem scales exponentially: 50 agents × 20 carriers = 1,000 separate credential sets to manage. Each credential has its own lifecycle — initial authorization, periodic refresh, handling revocation when agents leave the agency, and managing scope changes when carrier APIs update.

Traditional approaches use one of two flawed models:

System-level credentials: The agency application uses master credentials to access carrier portals on behalf of all agents. This creates security risks (credential compromise affects all agents), compliance problems (actions can't be attributed to individual agents), and functionality limitations (carrier systems often restrict what master accounts can do).

Agent-managed credentials: Each agent stores their own carrier passwords in the application. This shifts security burden to agents, creates credential management nightmares when passwords change, and makes automated workflows impossible when credentials expire.

Arcade's delegated authorization model solves both problems through OAuth-based per-agent, per-carrier token management:

• Agents authorize the AI agent to access each carrier portal using OAuth flows
• Tokens are encrypted and stored securely in Arcade's credential vault
• Refresh logic maintains valid tokens without agent intervention
• Revocation is simple — agent withdraws authorization through standard OAuth mechanisms
• Every carrier access is attributed to the specific agent, maintaining audit trails

For insurance agencies, this means deploying AI agents that actually work in production — executing cross-carrier queries, automating commission tracking, and aggregating client data without the authorization infrastructure nightmare.

Commission Tracking and Multi-Carrier Portfolio Management

Independent agents managing multi-carrier portfolios spend hours weekly tracking commissions, verifying policy counts, and reconciling carrier statements. Each carrier provides commission data in different formats, on different schedules, through different portals. Agents download spreadsheets, copy data into tracking systems, and manually calculate year-to-date totals.

An AI agent with secure access to all carrier portals can automate this entire workflow:

• Automated commission retrieval: Logging into each carrier portal, downloading latest commission statements
• Cross-carrier aggregation: Combining data across carriers in standardized format
• Variance detection: Identifying discrepancies between expected and actual commission
• Payment reconciliation: Matching commission statements to bank deposits
• Performance reporting: Generating dashboards showing production by carrier, line of business, and time period

This automation requires the agent to access each carrier portal with the agent's credentials, navigate to commission reporting sections, download appropriate files, and extract structured data — all while maintaining audit trails showing which agent's credentials were used for each access.

Building this without Arcade means implementing web scraping for 20+ carrier portals (brittle and frequently breaking when carriers update interfaces), managing credentials manually, and maintaining custom extraction logic for each carrier's data format.

With Arcade's authorization platform, agencies build carrier-specific tools once using the custom SDK, configure OAuth flows, and deploy agents that maintain their authorization as carrier systems evolve.

Security, Compliance, and Audit Requirements for Insurance AI Agents

Insurance AI agents handle sensitive personal information, financial data, and proprietary business systems. Security failures create regulatory violations, financial liability, and competitive disadvantages. Compliance isn't optional — it's the prerequisite for production deployment.

State insurance departments require insurers to maintain strict data protection, audit trails, and security controls. NAIC model laws define privacy requirements for customer information. SOC 2 audits validate that service providers maintain appropriate security controls. E&O insurance carriers review technology security when underwriting agency policies.

The security challenge compounds when agents need broad system access to deliver value. A claims processing agent requires access to policy databases, carrier portals, damage assessment tools, payment systems, and communication platforms. Traditional security models grant system-level access to applications, creating attack surfaces and compliance gaps when agents need to act on behalf of many users with different permission levels.

Insurance AI agents must implement:

• Zero token exposure: LLMs never see API keys, OAuth tokens, or database credentials
• Delegated authorization: Agents inherit user-specific permissions, not system admin access
• Just-in-time credential retrieval: Tokens accessed only at execution time, not stored in agent context
• Granular scope enforcement: Tools receive only the permissions necessary for specific actions
• Complete audit trails: Every agent action logged with user context, timestamp, and outcome
• User approval workflows: Sensitive operations require explicit human authorization before execution

Building these controls without a purpose-built platform means implementing OAuth flows, token lifecycle management, permission scoping, and audit logging for every integrated system — multiplied across dozens of tools and hundreds or thousands of users.

How Arcade Ensures Tokens Are Never Exposed to LLMs

The fundamental security problem in AI agent architectures is that LLMs need to call tools, but tools require credentials, and giving LLMs access to credentials creates unacceptable risks. An LLM with database credentials could leak them in generated text. An LLM with OAuth tokens could use them in ways users never authorized.

Arcade's architecture eliminates this risk through strict separation between reasoning and execution:

1. Agent requests tool execution: LangChain agent decides to call a tool but doesn't have credentials
2. Arcade validates authorization: Confirms the user has granted permission for this specific tool
3. Arcade retrieves scoped token: Fetches encrypted credential with appropriate permissions
4. Arcade executes action: Calls the tool API on behalf of the user
5. Arcade returns results: Sends tool output back to agent without exposing credentials

At no point do credentials enter the LLM context. The agent sees only tool definitions (what actions are possible) and tool results (what happened), never the authorization tokens required to execute actions.

This zero-token-exposure architecture is validated through SOC 2 Type 2 certification:

• Just-in-time authorization validated by independent auditors
• Tool-level access controls that inherit from existing identity providers
• Complete audit trails for every agent action
• VPC deployment options for air-gapped environments

For insurance companies, this means AI agents can access sensitive systems with the same security guarantees as human users — delegated permissions, scope-limited access, and full audit trails.

Maintaining Regulatory Compliance and Audit Trails

Insurance regulators require complete audit trails documenting system access, data modifications, and decision-making processes. When an AI agent processes a claim, approves underwriting, or modifies customer records, the insurer must be able to document:

• Which user authorized the agent to take this action
• What specific permissions were granted
• When the action occurred
• What data was accessed or modified
• What decision logic the agent applied
• Whether human approval was obtained for sensitive operations

Traditional application logging captures some of this information, but gaps emerge when systems lack user-specific action attribution or when credentials are shared across users.

Arcade's authorization platform maintains complete audit trails by design:

• Every tool call is logged with authenticated user context
• Token issuance and refresh events are recorded
• Permission scopes are documented for compliance review
• Just-in-time approval workflows maintain records of human authorization
• Logs are immutable and available for regulatory audit

For insurance companies preparing for state examinations or SOC 2 audits, this compliance documentation is built into the platform rather than requiring custom implementation.

Building Your First Insurance AI Agent: Where to Start

Insurance companies should begin with a single, well-defined use case rather than attempting comprehensive automation immediately. This approach:

• Validates technical infrastructure before broad deployment
• Demonstrates business value to secure stakeholder buy-in
• Identifies integration challenges in a contained scope
• Builds team expertise with manageable complexity
• Establishes compliance patterns for future expansion

A typical starting point might be FNOL automation for a specific claims type, commission tracking for independent agents, or renewal outreach for personal lines customers. These use cases deliver measurable value while limiting scope and risk.

Integrating Custom Insurance Tools and Carrier Portals

Insurance companies operate proprietary policy management systems, claims platforms, underwriting tools, and carrier portal integrations that aren't available as pre-built connectors. Building AI agents that deliver real business value requires secure access to these custom systems.

Arcade's MCP framework enables insurance teams to wrap internal policy, claims, and carrier systems as authenticated agent tools without rebuilding the underlying multi-user authorization infrastructure. Teams focus on defining what actions the tool exposes and how it interacts with internal systems, while Arcade manages token and secret management, scoped multi-user authorization, and operational concerns. Tools do not have to be in the shared tool catalog to be used in production workflows, which is critical for proprietary insurance platforms and custom carrier integrations.

Frequently Asked Questions

How does Arcade.dev handle authorization for thousands of independent insurance agents accessing multiple carrier portals?

Arcade implements per-agent, per-carrier OAuth token management where each independent agent authorizes the AI system to access their credentials at each carrier through standard OAuth flows, tokens are encrypted and stored in Arcade's secure credential vault with automatic refresh logic, and every carrier portal access is attributed to the specific agent maintaining complete audit trails. This eliminates the need for agencies to build custom OAuth implementations for 20+ carrier platforms or manage credential storage, refresh handling, and compliance documentation manually.

What security controls should insurance companies implement when deploying AI agents that process claims or underwriting decisions?

Insurance AI agents require layered security controls including zero-token-exposure architecture where LLMs never see credentials, delegated authorization ensuring agents inherit user-specific permissions rather than system-level access, and just-in-time approval workflows for high-value transactions. Deploy confidence thresholds that escalate uncertain decisions to human adjusters, implement user approval gates for actions above defined dollar limits, and start with minimum viable agent scope to prove security controls work correctly before expanding capabilities.

Can insurance companies run LangChain and Arcade agents inside their own controlled environments to meet data residency requirements?

Yes. Arcade's MCP runtime can run inside your own controlled environments, including highly restricted or regulated networks, while preserving the same multi-user authorization patterns and tool catalog. This lets insurance teams meet data residency and regulatory requirements without changing agent logic or compromising security standards.

How long does it take to build custom tools for proprietary insurance policy management systems or claims platforms?

Arcade's MCP framework lets teams expose proprietary policy and claims systems as tools without rebuilding multi-user authorization and credential management from scratch. Instead of investing weeks into OAuth flows, token lifecycle handling, and audit logging for each system, insurance teams focus on defining the business actions they want agents to perform while Arcade handles tokens, secrets, scoped permissions, and security controls.