Using LangChain and Arcade.dev to Build AI Agents For Asset & Wealth Management: Top 3 Use Cases

Key Takeaways

• The AI agents market in financial services will grow from $490.2 million in 2024 to $4.48 billion by 2030, yet production deployment stalls at authorization: AI agents can help wealth advisors reclaim up to 75% of the time they currently spend on routine tasks like portfolio reporting, compliance alerts, and client onboarding — tasks agents can automate if they can securely act across Gmail, CRMs, and custodian platforms. Arcade.dev's MCP runtime solves the multi-user authorization gap blocking production deployment
• LangChain provides orchestration, Arcade enables secure action: 51% of organizations already have AI agents in production, with LangChain emerging as the leading framework — but without Arcade's delegated multi-user authorization and scoped permissions, these agents can't safely execute workflows across fragmented, domain-specific financial systems
• Single use case implementation drives enterprise adoption: Firms achieving significant reductions in onboarding time and cost savings through 30 hours weekly per advisor start with one focused workflow — portfolio reporting, compliance monitoring, or client communication — then scale based on demonstrated ROI
• Multi-user authorization is the production blocker, not AI capability: While 78% of organizations have active plans to implement AI agents into production, multi-user authorization infrastructure for delegated user permissions, token lifecycle management, and audit trails requires 6-12 months of custom development — time competitors using Arcade spend refining agent intelligence
• Security and compliance separate pilots from production: Asset managers need zero token exposure to LLMs, complete audit trails for fiduciary compliance, and delegated permissions matching advisor access rights — infrastructure that Arcade's MCP runtime provides through just-in-time multi-user authorization, tool-level access controls, and VPC deployment options for regulated environments

The challenge facing asset and wealth management isn't whether AI agents can automate workflows — it's whether your firm can deploy them to production before competitors do. Most financial institutions build impressive proof-of-concepts demonstrating portfolio report generation or compliance alert summarization, then hit an authorization wall when attempting multi-user deployment.

Arcade.dev's tool-calling platform serves as the MCP (Model Context Protocol) runtime that enables and governs agent authorization across tools. When your LangChain agent needs to pull client holdings from Schwab, cross-reference performance data from Black Diamond, generate branded reports, and distribute via Gmail — Arcade handles the fine-grained, delegated multi-user authorization and scoped permissions that make these actions secure, auditable, and compliant.

Arcade never handles the underlying portfolio or client data itself; it focuses on managing the tokens and secrets that govern how agents access those existing systems.

The business impact is measurable: advisors reclaim 30 hours weekly through automation, mid-size RIAs achieve significant cost savings, and compliance teams reduce regulatory alert review from hours to minutes. AI/ML teams can focus on agent intelligence rather than plumbing, while security teams gain a single place to enforce policies and review every agent action. Yet 45.8% of small companies cite performance quality as their top deployment barrier — a problem rooted in multi-user authorization complexity, not AI capability.

Building this infrastructure without a purpose-built platform means assembling custom OAuth flows for every custodian platform, managing token refresh logic across hundreds of advisors, implementing audit trails for regulatory compliance, scoping permissions so agents access only what each user authorized, and handling edge cases when advisors leave the firm or revoke access. Teams attempting this path typically spend 6-12 months on multi-user authorization infrastructure before shipping their first production agent.

Redefining Asset & Wealth Management with AI Agents: Beyond Chat with Arcade.dev

AI agents differ fundamentally from chatbots in one critical dimension: chatbots respond to queries, while agents execute actions on behalf of users. In wealth management contexts, this means an agent doesn't just answer "what are this client's portfolio holdings?" — it authenticates to the custodian platform as the advisor, retrieves multi-account positions, calculates performance attribution, generates client-ready reports, and distributes them via the advisor's email.

This operational distinction matters because wealth management workflows span fragmented, domain-specific platforms. Advisors toggle between custodian portals, portfolio management systems, CRM platforms, financial planning software, email, calendars, and compliance documentation systems. A typical portfolio reporting workflow requires:

• Logging into multiple custodian platforms (Schwab, Fidelity, TD Ameritrade)
• Exporting position data and transaction histories
• Importing into portfolio management software for performance calculation
• Cross-referencing client goals and risk profiles from CRM
• Generating reports in presentation software
• Distributing via email with appropriate disclaimers
• Documenting the review for compliance records

Manual execution of this workflow consumes 2-4 hours weekly per advisor. For a 50-advisor firm, that represents 100-200 hours of labor redirected from client-facing activities. AI agents can automate these workflows while maintaining the human oversight necessary for fiduciary compliance.

But production deployment requires solving multi-user authorization at scale. When an AI agent acts, it needs delegated permissions that match the advisor's actual access rights — not system-level admin credentials. The agent serving 50 advisors must authenticate separately for each advisor's custodian accounts, CRM data, and email, respecting their individual permission boundaries and generating separate audit trails.

The Multi-User Authorization Challenge Blocking Production Deployment

Traditional chatbots avoid multi-user authorization complexity by remaining read-only and advisory. They can analyze data you provide but can't access your Gmail, query your CRM, or execute transactions on your behalf. Production wealth management agents require write access to mission-critical financial systems — which surfaces authorization challenges most firms can't solve with internal resources.

The core problem is multi-user authorization: enabling AI agents to securely act on behalf of different users with appropriate permission scoping. This requires:

• Delegated user credentials — each advisor's individual OAuth tokens, not shared system accounts
• Scoped tool access — reading client data doesn't grant permission to execute trades
• Just-in-time authorization — advisors approve sensitive actions before agent execution
• Comprehensive audit trails — every agent action logged with user context for compliance review
• Zero token exposure — credentials never passed to the LLM to prevent leakage

Building this infrastructure from scratch forces wealth management firms into problems outside their core expertise: implementing OAuth 2.0 flows for Salesforce, HubSpot, Gmail, and proprietary custodian APIs; managing token refresh, expiration, and revocation across hundreds of advisors; enforcing permission scopes so agents access only authorized data; maintaining compliance documentation for every multi-user authorization pattern; handling edge cases when advisors change firms or clients revoke consent.

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

Why 'Go Beyond Chat' Matters for Competitive Advantage in Finance

The wealth management industry faces structural pressure from multiple directions: fee compression from robo-advisors, rising client expectations for digital experiences, regulatory compliance costs, and talent acquisition challenges. AI agents address these pressures by automating routine workflows while preserving the high-touch advisory relationships that justify premium fees.

Advisors who can reclaim significant time from portfolio reporting, compliance monitoring, and administrative coordination can scale their practice and deepen client relationships. AI agents that actually execute these workflows — not just provide advice about them — reclaim capacity for revenue-generating activities.

The competitive advantage compounds over time. Early adopters automating portfolio reporting gain 30 hours weekly per advisor for client acquisition and relationship management. Firms that automate compliance monitoring catch regulatory issues before they become violations. Organizations deploying client onboarding agents compress 2-3 week timelines to days, improving client experience and advisor productivity simultaneously.

But capturing this value requires moving beyond chatbot demos to production-grade agent deployments — which is why authorization infrastructure becomes the critical path dependency for competitive advantage.

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

LangChain has emerged as the leading framework for building AI agents, with 90M+ monthly downloads and broad adoption across financial services. The framework excels at orchestrating multi-step agent workflows, managing conversation context, and coordinating specialized sub-agents for different tasks. LangGraph — the graph-based orchestration layer built on LangChain — introduces conditional logic and state management that enables agents to handle complex financial workflows like portfolio rebalancing or compliance alert triage. In asset and wealth management, LangGraph coordinates these multi-step flows while Arcade enforces the multi-user authorization and scoped permissions that let agents take real actions in custodian, CRM, and communication systems.

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

LangChain's Role: Agent Orchestration and Multi-Step Reasoning

• Decomposes complex tasks into manageable steps (analyze portfolio → identify rebalancing needs → generate trade proposals)
• Manages agent state and conversation history across multi-turn interactions
• Routes decisions through conditional logic based on data analysis and business rules
• Coordinates multiple specialized agents for different workflow components

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

• Provides governed, OAuth-enabled tool access to Gmail, Slack, Salesforce, HubSpot, custodian platforms, and proprietary financial systems
• Manages delegated user credentials through token and secret management without exposing them to LLMs or handling the underlying financial data
• Enforces scoped permissions and human-in-the-loop approval workflows for sensitive operations
• Maintains immutable audit trails for regulatory compliance and fiduciary review
• Handles complete token lifecycle including refresh, expiration, and revocation

This separation of concerns lets wealth management firms focus on agent intelligence and business logic rather than rebuilding authorization infrastructure. Instead of implementing custom OAuth flows for every platform, teams use Arcade's pre-built connectors for standard business systems and the custom SDK for proprietary portfolio management or custodian platforms.

LangChain's Agent Orchestration Capabilities for Financial Workflows

LangChain enables wealth management AI agents to decompose complex financial tasks into sequential or parallel steps, maintain context across extended conversations, and make intelligent decisions about which tools to invoke based on intermediate results.

For a portfolio review agent, LangChain might orchestrate:

1. Data retrieval: Pulling current positions from multiple custodian platforms simultaneously
2. Performance calculation: Analyzing returns attribution across asset classes and time periods
3. Goal comparison: Cross-referencing results against client objectives from CRM
4. Drift analysis: Calculating allocation deviations from target portfolio
5. Recommendation generation: Proposing rebalancing trades or strategy adjustments
6. Report compilation: Generating client presentation with charts and commentary
7. Distribution: Sending final report via advisor's email with compliance disclaimers

LangGraph extends basic chaining with visual flow control and conditional branches. When the agent detects portfolio drift exceeding policy thresholds, it can route to human review. When rebalancing recommendations involve tax consequences, it can escalate to the advisor for approval before generating trade proposals. This transparency makes financial workflows auditable and debuggable — critical for regulatory validation.

The framework's strength is orchestration logic and intelligent decision-making. LangChain assumes tools are already accessible with appropriate permissions — an assumption that holds for internal demos but fails in production multi-user environments where different advisors need different access levels to the same client data.

Arcade's Secure Tool Execution and Zero Token Exposure Architecture

Arcade solves the authorization gap by serving as the MCP runtime layer between LangChain agents and the financial systems they need to access. When a wealth management AI agent calls a tool, Arcade:

1. Validates user authorization: Confirms the advisor has granted the agent permission for this specific tool and action
2. Retrieves scoped credentials: Fetches encrypted OAuth token with appropriate permission boundaries from secure storage
3. Executes the tool call: Invokes the API (query CRM, send email, retrieve portfolio data) on behalf of the advisor
4. Logs the complete action: Records who, what, when, and why in immutable audit trail
5. Returns only results: Sends tool output back to LangChain agent without exposing any credentials

This zero-token-exposure architecture ensures LLMs never see API keys, OAuth tokens, or database credentials. The AI model receives only tool definitions describing available actions and tool results showing what happened — never the authorization tokens required to execute those actions.

For wealth management deployments, this means AI agents can access sensitive client data and execute financial workflows with the same security guarantees as human advisors: delegated permissions matching individual access rights, scope-limited authorization preventing unauthorized actions, and complete audit trails satisfying fiduciary documentation requirements.

With SOC 2 Type 2 certification, Arcade.dev becomes the authorized path to production with these validated controls: just-in-time multi-user authorization verified by independent auditors, tool-level access controls that inherit from existing identity providers, complete audit trails for every agent action, and VPC deployment options for air-gapped environments.

Use Case 1: Automating Portfolio Reporting and Client Communication

Portfolio reporting represents the highest-value automation opportunity for wealth management firms. Advisors spend 2-4 hours weekly manually compiling client reports from multiple custodian platforms, cross-referencing performance data, calculating attribution, and formatting presentations. This workflow fragments advisor attention and delays client communication.

For a 50-advisor RIA, manual portfolio reporting consumes 100-200 hours of professional time weekly — capacity that could instead drive client acquisition, relationship deepening, or financial planning conversations. The business case for automation is straightforward: significant time reduction equals 30 hours reclaimed per advisor weekly, representing substantial annual savings for mid-size firms.

An AI agent with MCP-governed access to custodian APIs can automate the complete workflow:

• Use delegated access to multiple custodians as the advisor via OAuth credentials managed by the multi-user authorization layer
• Query holdings and transactions across Schwab, Fidelity, TD Ameritrade accounts
• Calculate performance metrics including returns attribution, benchmark comparison, asset allocation drift
• Cross-reference client goals from CRM to contextualize results
• Generate branded reports using firm templates and compliance-approved language
• Distribute via email from advisor's account with appropriate disclaimers
• Document the workflow in audit trail for compliance review

The agent operates within each advisor's existing permission boundaries. When serving 50 advisors, it uses separate delegated access for each advisor's custodian credentials, CRM access, and email — respecting individual permission scopes rather than using system-level admin access that creates security and compliance risks.

Multi-Custodian Data Aggregation with Delegated Authorization

The technical challenge in portfolio reporting automation is multi-custodian data aggregation with proper authorization. Client portfolios often span accounts at Schwab, Fidelity, TD Ameritrade, and other platforms. Each custodian maintains separate API access requiring individual OAuth flows and credential management.

Traditional approaches grant the application system-level API access, creating security risks when one agent serves many advisors. If the agent has direct database credentials or admin-level API keys, any advisor could potentially access any client account through agent queries — violating need-to-know principles and creating liability.

Arcade's delegated authorization model enforces advisor-specific permissions. When Advisor Smith queries client portfolios through an AI agent, the agent inherits Smith's custodian credentials and access rights — retrieving only accounts Smith is authorized to view. When Advisor Jones uses the same agent, it operates within Jones's permission boundaries, accessing a completely different set of client accounts.

This authorization pattern requires:

• OAuth integration with each custodian's API platform
• Secure credential storage with encryption at rest and in transit
• Token lifecycle management including refresh and revocation handling
• Scoped permission enforcement matching advisor access levels
• Just-in-time credential retrieval at execution time rather than persistent storage in agent context

Building this infrastructure for a single custodian requires substantial engineering effort. Scaling it across the dozens of financial platforms wealth management agents need to access — custodians, portfolio management systems, CRMs, financial planning software, compliance databases — creates compounding complexity that Arcade's platform abstracts away through its pre-built connectors and custom SDK.

Automated Report Generation and Email Distribution via Gmail Integration

Once the agent aggregates portfolio data from multiple custodians, it needs to generate client-ready reports and distribute them via the advisor's email. This requires secure access to document generation tools and email systems with proper advisor attribution.

An AI agent for Gmail demonstrates this pattern. The agent needs delegated access to:

• Gmail API: Sending emails from advisor's account with proper signature and disclaimers
• Document generation: Creating PDF reports using firm templates and branding
• CRM integration: Recording sent reports in client communication history

Arcade handles the OAuth flows for Gmail while providing frameworks for integrating document generation and CRM systems. The agent operates with each advisor's credentials, respecting their existing access permissions and generating audit records for every email sent.

The business impact scales with firm size. A 50-advisor firm executing weekly portfolio reports achieves 100-200 hours of labor recapture weekly. This capacity redirects toward revenue-generating activities: prospecting new clients, conducting financial planning reviews, or deepening relationships with high-value households.

Use Case 2: Compliance Alert Summarization and Regulatory Monitoring

Compliance teams in wealth management firms manually review 220+ regulatory alerts daily from FINRA, SEC, state regulators, and industry organizations. This workflow consumes hours of professional time while creating risk that critical updates get missed in the volume.

Each alert requires contextual analysis: Does this apply to our firm's registration states? Does it impact products we offer? Do our client demographics create exposure? Should we update policies or procedures? Manual review struggles with consistency and comprehensiveness, especially as regulatory complexity increases.

AI agents can automate this workflow through intelligent filtering, relevance scoring, and priority routing:

• Ingest regulatory alerts from FINRA, SEC, state regulators via authenticated feeds
• Cross-reference firm profile including registration states, product offerings, client segments from CRM data
• Apply relevance scoring based on products offered, client demographics, business model
• Route high-priority alerts to compliance team via Slack or email with context and recommended actions
• Maintain alert history in compliance database with complete audit trail
• Generate summary reports for quarterly compliance review

The agent operates across multiple systems with appropriate authorization: regulatory data feeds, CRM platforms for firm profile data, communication tools like Slack for team notifications, and compliance documentation systems for recordkeeping.

Cross-Referencing Regulatory Updates Against Firm Operations in CRM Systems

The intelligence in compliance monitoring comes from cross-referencing regulatory alerts against firm-specific context. A new FINRA rule about cryptocurrency marketing only matters if the firm offers crypto investments. An SEC advisory about fee disclosure impacts advisory firms but not broker-dealers operating on commission.

This contextual analysis requires the agent to access:

• CRM data: Product offerings, client demographics, account types
• Registration records: States where the firm is registered, product approvals
• Policy documentation: Current procedures that might need updates
• Historical alerts: Previous regulatory actions in related areas

Arcade's integration with platforms like HubSpot and Salesforce enables agents to query CRM data with delegated user permissions. When the compliance officer runs the agent, it accesses the full firm profile. When an advisor queries alert relevance for their book of business, it filters to their specific client segment.

This same authorization model extends to communication platforms. The agent can post high-priority alerts to a dedicated Slack channel, route urgent issues via email, and maintain compliance documentation — all with appropriate audit trails showing who received which information and when.

Real-Time Slack Notifications for High-Priority Regulatory Changes

Compliance teams benefit from real-time alerting when critical regulatory changes occur. An AI-powered Slack agent can monitor regulatory feeds, evaluate relevance, and immediately notify appropriate personnel through Slack channels or direct messages.

The workflow operates continuously:

• Monitor regulatory sources for new alerts and guidance
• Evaluate relevance using firm profile criteria
• Assign priority scores based on impact assessment
• Post to Slack with summary, affected areas, and recommended actions
• Tag appropriate personnel for urgent issues
• Maintain thread history for team discussion
• Document decisions and actions in compliance database

This real-time notification system ensures critical updates don't wait for batch processing or daily review cycles. When FINRA issues emergency guidance on cybersecurity incidents, the agent immediately alerts the compliance team and information security personnel, accelerating response time.

The Archer Slack agent demonstrates this pattern with out-of-the-box integrations for Gmail, Google Calendar, and other business systems, plus customization for compliance-specific databases and regulatory feeds.

Use Case 3: Client Onboarding Automation and KYC Processing

New client onboarding represents significant administrative burden for wealth management firms. The process typically spans 2-3 weeks and consumes 5-8 hours of advisor time per client, involving:

• Gathering personal information and account documentation
• Verifying identity through KYC procedures
• Assessing investment objectives and risk tolerance
• Opening accounts across multiple custodian platforms
• Establishing initial portfolio allocations
• Scheduling initial review meetings and ongoing service calendar

This manual workflow creates delays that impact client experience and prevents advisors from focusing on relationship development. Firms achieving significant reductions in onboarding time through automation improve both operational efficiency and client satisfaction.

An AI agent can orchestrate the complete onboarding workflow while maintaining necessary human oversight for regulatory compliance:

• Populate CRM records from intake forms with data validation and completeness checking
• Submit KYC documentation to compliance systems with appropriate approvals
• Open custodian accounts via API with delegated advisor credentials
• Schedule initial meetings via Google Calendar with automated confirmations
• Generate onboarding checklists tracking progress and outstanding items
• Document complete workflow in audit trail for compliance review

The agent operates across fragmented systems with MCP-governed authorization: CRM platforms, compliance databases, custodian APIs, calendar systems, and communication tools. Each action respects user permissions and generates audit records satisfying regulatory requirements.

Automated Account Opening Across Multiple Custodian Platforms

The most complex aspect of client onboarding is opening accounts across multiple custodian platforms. A client might need:

• Taxable investment account at Schwab
• Roth IRA at Fidelity
• 401(k) rollover IRA at TD Ameritrade
• 529 education savings account

Each custodian maintains separate account opening APIs with unique authentication, data formatting, and approval workflows. Traditional automation approaches use screen scraping or robotic process automation (RPA), creating brittle integrations that break with interface changes.

AI agents with secure API access can open accounts programmatically while maintaining proper authorization and audit trails. The agent:

• Authenticates to custodian APIs using advisor's delegated credentials
• Submits account opening requests with validated client data
• Handles custodian-specific formatting and requirements
• Tracks approval status and resolves issues
• Documents complete process for compliance review

This requires Arcade's custom SDK to wrap custodian APIs as authenticated agent tools. While major custodians increasingly offer APIs, many require custom integration work. The SDK enables wealth management firms to build these integrations without reimplementing OAuth flows, token management, and audit logging for each platform.

Calendar Integration for Scheduling Initial Review Meetings

Client onboarding concludes with scheduling initial portfolio review meetings and establishing ongoing service calendar. This coordination requires:

• Checking advisor availability across existing client meetings
• Proposing times that work for client schedules
• Sending calendar invitations with meeting details
• Setting up automated reminders and follow-up sequences
• Documenting scheduled activities in CRM

A Google Calendar agent can automate this workflow through natural language interaction. The advisor or client service associate describes scheduling requirements in conversational language, and the agent handles the coordination:

• Query advisor calendar for availability
• Send meeting proposals to client via email
• Create calendar events upon confirmation
• Set automated reminders at appropriate intervals
• Record scheduled meetings in CRM for service tracking

The agent operates with delegated Google Calendar credentials, accessing only the advisor's calendar and respecting their permission boundaries. When serving multiple advisors, it authenticates separately for each advisor's calendar rather than using shared system access.

Building Secure and Compliant AI Agents for Financial Services

AI agents in wealth management handle sensitive client data, proprietary investment strategies, and regulated financial activities. Security failures create regulatory violations, reputational damage, and legal liability. Compliance isn't optional — it's the prerequisite for production deployment in financial services.

The security challenge compounds when agents need broad system access to deliver value. A portfolio reporting agent requires access to custodian platforms, CRM databases, email systems, and document repositories. Traditional security models that grant system-level credentials to applications create unacceptable risks when agents serve multiple advisors with different permission levels.

Asset managers deploying production agents need:

• Zero token exposure to LLMs — preventing credential leakage in generated text or model outputs
• Delegated user authorization — agents inherit advisor-specific permissions, not admin access
• Just-in-time credential retrieval — tokens accessed only at execution time, never stored in agent context
• Scoped permission enforcement — tools receive only the access necessary for specific actions
• Immutable audit trails — every agent action logged with user context, timestamp, input, and result
• Human-in-the-loop workflows — sensitive operations require explicit advisor approval before execution

Building these controls without a purpose-built platform means implementing OAuth flows for every integrated system, managing token lifecycle across hundreds of users, enforcing granular permissions, maintaining regulatory-grade audit logs, and handling edge cases like token revocation or user termination.

Audit Trails and Regulatory Compliance Documentation

Wealth management firms operate under strict fiduciary duties requiring complete documentation of investment decisions, client communications, and operational activities. AI agents executing these workflows must maintain audit trails meeting regulatory standards.

The audit requirements include:

• Who: Which advisor or user initiated the agent action
• What: Specific action taken (query portfolio, send email, update CRM)
• When: Timestamp with appropriate precision for sequencing
• Why: Context explaining the business purpose or client need
• Result: Outcome including success, failure, or human intervention

Arcade's architecture generates these audit records automatically for every tool execution. When an agent queries client portfolio data from a custodian, the log captures the advisor identity, specific API call, data retrieved, and timestamp. When the agent sends an email, the record includes recipient, subject, body, and delivery confirmation.

These logs feed compliance monitoring systems and regulatory examination responses. Firms can demonstrate that AI agent actions respected user permissions, followed approval workflows, and maintained the same oversight as human-executed activities.

For asset managers, this means AI agents can automate workflows while satisfying compliance obligations — capturing the efficiency gains without creating regulatory gaps.

Leveraging LangChain for Agentic Orchestration in Wealth Management

LangChain provides the orchestration framework enabling wealth management AI agents to decompose complex financial tasks, coordinate multiple specialized sub-agents, and make intelligent decisions about tool usage based on intermediate results.

The framework's architecture supports sophisticated agent patterns:

• Sequential chains: Multi-step workflows where each step builds on previous results (retrieve data → analyze → generate recommendations → create report)
• Parallel execution: Simultaneous queries to multiple systems for faster completion (query all custodians concurrently rather than sequentially)
• Conditional routing: Decision points that direct workflow based on data analysis or business rules (if portfolio drift exceeds threshold, route to advisor approval)
• Multi-agent coordination: Specialized sub-agents handling different workflow components (research agent, compliance agent, reporting agent working together)
• Memory and context management: Maintaining conversation history across extended interactions for coherent multi-turn experiences

For wealth management use cases, this orchestration capability enables agents that handle complex financial workflows requiring judgment, context awareness, and adaptability — capabilities beyond simple automation or rule-based systems.

LangGraph for Complex Multi-Step Financial Workflows

LangGraph — the graph-based orchestration layer built on LangChain — introduces visual flow control and state management that makes complex agent workflows transparent and debuggable. Instead of linear chains, LangGraph enables conditional branches, loops, and parallel paths based on intermediate results.

For a portfolio rebalancing agent, LangGraph might define:

1. Retrieve current positions from all custodian platforms
2. Calculate allocation drift from target portfolio
3. If drift < threshold: Exit workflow, no action needed
4. If drift >= threshold: Proceed to rebalancing logic
5. If tax implications detected: Route to advisor review
6. If no tax concerns: Generate trade proposals automatically
7. Submit for advisor approval before execution
8. Execute approved trades via custodian APIs
9. Generate confirmation reports and update CRM
10. Schedule follow-up review via calendar

Each decision point represents conditional logic based on financial analysis or business rules. The visual graph representation makes the workflow auditable for compliance review and debuggable when issues arise.

This transparency matters for regulatory validation. Compliance teams can review the agent's decision logic, verify it follows firm policies, and document the approval gates built into automated workflows.

Integrating Legacy Systems and Modern APIs in Financial Infrastructure

Wealth management technology infrastructure fragments across generations of platforms: legacy portfolio management systems from the 1990s, cloud-based CRMs from the 2010s, modern API-first custodian platforms, and proprietary tools built internally over decades. AI agents need access to all these systems to deliver comprehensive automation.

The integration challenge compounds when systems lack modern APIs or OAuth support. Many custodian platforms and portfolio management tools predate current authentication standards, offering only username/password access, session-based authentication, or proprietary API keys without granular scoping.

Arcade's platform addresses this through multiple integration pathways:

• Pre-built connectors for common business systems (Gmail, Slack, Salesforce, HubSpot, Google Calendar)
• Custom SDK for wrapping proprietary APIs as authenticated agent tools
• OAuth adapters for systems with legacy authentication that can be wrapped in modern flows
• Database connectors for direct SQL access with appropriate scoping and audit logging

This flexibility enables wealth management firms to build agents that work with their actual technology stack rather than requiring complete platform modernization before automation becomes possible.

Building Custom MCP Servers for Proprietary Portfolio Management Systems

Many wealth management firms use specialized portfolio management platforms like Black Diamond, Orion, Tamarac, or proprietary systems built internally. These platforms contain critical data for portfolio reporting, performance analysis, and client communication — but may lack modern APIs or OAuth support.

Arcade's custom tool SDK enables firms to wrap these systems as authenticated agent tools without rebuilding complete authorization infrastructure. The development pattern follows:

1. Identify required actions: What should the agent be able to do? (query holdings, retrieve performance, update allocations)
2. Map to existing APIs: How do these actions translate to specific system calls?
3. Implement authorization: How should advisor permissions map to system access?
4. Build tool wrapper: Integrate using Arcade's SDK
5. Test and validate: Use evaluation frameworks to verify correct behavior
6. Deploy with monitoring: Track performance and errors in production

For well-documented REST APIs with OAuth support, basic query tools require minimal development time. More complex integrations with legacy systems might take longer, but teams spend time on business logic and domain expertise rather than reimplementing OAuth flows, token refresh handlers, and audit logging for each integration.

The critical advantage is portability — once wrapped as Arcade tools, these integrations work consistently across different agents, frameworks, and deployment environments. A portfolio query tool built for one agent becomes reusable across compliance monitoring, client reporting, and performance analysis agents.

Getting Started: Blueprint for Wealth Management Teams

Wealth management firms should begin AI agent deployment with a focused pilot addressing a single high-value use case rather than attempting comprehensive automation immediately. This approach validates technical infrastructure, demonstrates measurable ROI to stakeholders, identifies integration challenges in contained scope, and builds team expertise with manageable complexity.

Typical starting points include:

• Portfolio reporting automation for a single advisor group or client segment
• Compliance alert summarization for regulatory monitoring team
• Client onboarding for one service tier or product line

These use cases deliver measurable business value while limiting technical scope and organizational change management. Teams prove the architecture works correctly, satisfy security and compliance requirements, and establish patterns for scaling to additional workflows.

Identifying Your First High-Value Use Case

The selection criteria for initial AI agent deployment should balance:

Business impact: Time saved, cost reduction, error elimination, or revenue enablement Technical feasibility: System access available, data quality sufficient, integration complexity manageable Compliance risk: Regulatory implications well-understood, audit requirements clear, oversight processes defined Stakeholder alignment: Executive sponsorship secured, affected teams on board, success metrics agreed

Portfolio reporting automation typically scores well across these dimensions: high business impact (30 hours weekly per advisor), moderate technical complexity (custodian APIs exist), clear compliance requirements (documented in advisory agreements), and broad stakeholder support (advisors want time back for client-facing activities).

Firms should implement a single use case to production, validate it delivers expected value, then scale to additional workflows. This staged approach reduces risk and builds confidence in the technology before making broader organizational commitments.

Arcade's Tool Catalog and MCP Framework

Arcade's platform includes a tool catalog of production-tested integrations for common enterprise systems — productivity tools like Gmail and Google Calendar, CRM platforms such as Salesforce and HubSpot, communication tools including Slack and Microsoft Teams, and major databases. Each tool centralizes multi-user authorization, token and secret management, permission scoping, and audit logging, so firms don’t have to rebuild this layer for every integration.

For proprietary systems like custodian platforms or portfolio management tools, Arcade’s MCP framework lets teams build and run custom tools that aren’t in the catalog, bringing those systems under the same governed multi-user authorization model. Leadership teams see a consistent control plane across standard SaaS platforms and bespoke infrastructure, while AI/ML and engineering teams avoid months of custom integration work per system.

Frequently Asked Questions

How does multi-user authorization differ from standard API authentication in wealth management deployments?

Standard API approaches often grant system-level access where one set of credentials serves all users, creating compliance risks when advisors should only access their own client data. Multi-user authorization means each advisor maintains individual OAuth tokens scoped to their specific permissions — when Advisor Smith's agent queries client portfolios, it retrieves only Smith's authorized accounts, while Advisor Jones's agent accessing the same tool sees completely different data matching Jones's permission boundaries, exactly as required for fiduciary compliance and regulatory oversight.

Can wealth management firms deploy LangChain and Arcade agents in private cloud or on-premises environments?

Yes. Your infrastructure teams can run the MCP runtime inside your own controlled environments that meet regulatory and security requirements, while keeping the same multi-user authorization model and tool catalog. Deployment choices remain an internal IT decision; business, compliance, and security leaders see consistent controls regardless of where the runtime is hosted.

What prevents AI agents from accessing unauthorized financial data when multiple advisors use the same tool?

Arcade's delegated multi-user authorization architecture retrieves credentials at execution time based on the specific user invoking the agent — when the agent calls a custodian API, Arcade validates which advisor initiated the request, fetches that advisor's encrypted OAuth token with their scoped permissions, executes the API call within those boundaries, and returns only results without exposing credentials. The LLM never sees tokens, advisors only access data they're authorized to view, and every action generates audit records showing exactly who accessed what information and when, satisfying fiduciary documentation requirements.

How long does implementation typically take for a wealth management firm's first AI agent use case?

A focused pilot implementing portfolio reporting automation or compliance monitoring for a single advisor group typically requires 4-6 weeks for development and testing, followed by 1-2 weeks for initial deployment and advisor training. Firms using Arcade's pre-built connectors for Gmail, Salesforce, and other standard platforms avoid months of custom OAuth development, while teams building custom integrations for proprietary custodian or portfolio management systems should budget additional time based on API documentation quality and system complexity — though most custom tools using Arcade's SDK require less development time than reimplementing authorization infrastructure from scratch.

What security controls should firms implement beyond Arcade's zero token exposure architecture?

Production wealth management agents require layered controls: tracing and observability for monitoring agent decisions and tool usage, human-in-the-loop approval workflows for sensitive operations like account changes or large transactions, confidence thresholds that escalate uncertain decisions to advisor review, comprehensive audit logging feeding compliance monitoring systems, and regular evaluation using testing frameworks to validate agent accuracy. Start with minimum viable agent scope proving security controls work correctly, then expand capabilities incrementally rather than granting broad permissions upfront — firms achieving successful deployments implement one focused use case to production, validate it meets security and compliance requirements, then scale based on demonstrated risk management.

How does Arcade handle token refresh and revocation when advisors change firms or clients terminate relationships?

Arcade manages complete token lifecycle including automatic refresh before expiration, immediate revocation when users disconnect access, and graceful error handling when tokens become invalid. When an advisor leaves the firm and their OAuth connections are terminated, their agents immediately lose access to those systems without manual intervention or lingering credentials — the next tool execution attempt validates multi-user authorization, finds no valid token, and returns an access error that routes to the appropriate workflow (notify an administrator, prompt for reauthorization, or gracefully skip unavailable data sources). This automatic lifecycle management eliminates the security risk of orphaned credentials while maintaining audit trails showing exactly when access was granted, used, and revoked for regulatory compliance documentation.