Why Building Multi-Agent AI Systems for your FSI Isn’t Always the Answer (& How to Approach it When it is)

If you are asking how to build a multi-agent AI system, you’ve probably hit the limits of off-the-shelf tools like ChatGPT or Perplexity Finance that can’t execute multi-step decisions across teams, tools, and data silos. Most likely, you want to automate complex, regulated workflows, but are unsure if your in-house team knows how to design, secure, integrate and scale them.

But a multi-agent system isn’t the answer for every financial services use case. 

As an AI-enablement company with experience implementing over 100 custom AI solutions across banking, insurance, and wealth, we know when a multi-agent system makes sense. In this article, we’ll help you understand if a multi-agent system is the right choice and how to design one with a secure, scalable architecture.

In this article:

• Does Building a Multi-Agent System Make Sense?
• Why Sector Complexity Defines How you Design Systems with Multiple Agents
• How to Approach Building Multi-Agent Systems for your FSI
• How to Develop Multi-Agent Systems for a Nondeterministic Sector like Wealth Management
• Why Build Custom Multi-Agent Systems with Neurons Lab

Want to build a tailored, effective and compliant multi-agent AI system? Neurons Lab can help. Book a call with us today.

Does Building a Multi-Agent System Make Sense?

Before understanding how to build a multi-agent AI system for financial services, it helps to know when one is actually needed. If your goal is to cut costs by automating repetitive support tasks, a retrieval-augmented generation (RAG) model is usually enough.

However, when your AI must integrate data and actions from several systems (e.g., comparing products across banks, personalizing recommendations, or completing transactions) you need a multi-agent system to manage the broader, more complex customer journey.

Unlike a single-agent model that handles only isolated tasks like a FAQ chatbot, a multi-agent system distributes the complexity of multi-step workflows across a group of specialized agents. Each of these specialized agents performs a defined function and works together with the other agents to deliver precise, domain-specific outcomes. 

This creates an “AI mind”—a coordinated AI system that can reason, make decisions, and use the right tools to execute complex workflows.

In financial services, these systems are valuable when the workflow crosses departments or combines rule-based and market-driven behaviors (e.g., comparing products across banks, executing transactions, managing insurance claims).

However, multi-agent systems also introduce higher complexity and stricter operational requirements, such as coordinating actions across risk, compliance, and product systems in real time. They should be considered only when a single-agent model or RAG-based approach can no longer meet your service, compliance, or integration needs.

How Sector Complexity Shapes Multi-Agent AI Design in Financial Services

Before designing a multi-agent AI system, it’s important to understand how your line of business shapes the system’s logic. Financial sectors differ in how predictable their operations are, and that difference determines how agents are designed, what data they access, and which tools they use.

For instance, in insurance or retail banking, most operations follow rule-based logic. Policy rates, premiums, and loan terms are calculated using fixed formulas and compliance frameworks, producing predictable outcomes. While still complex multi-step workflows, these are deterministic systems, meaning that if you always use the same data, you’ll always get the same result. 

A simple FAQ chatbot workflow – Image source: Medium

Take an insurance claims processing agent as an example. They need to validate claim eligibility, calculate reimbursement based on policy rules, and route the case to human review if it exceeds a certain threshold. They will always produce the same output when given the same input.

An example of a claims processing workflow – Image Source: Value Momentum

But sectors like wealth or asset management are different. Here, outcomes are unpredictable because they depend on variables like financial markets that fluctuate and can’t be controlled. These are nondeterministic systems, where the same input can produce different results depending on changing market conditions, timing, or investor behavior.

This distinction between deterministic and nondeterministic is crucial when building multi-agent architecture because it defines how you design your agents, what data they access, and which services/tools they use to arrive at reliable outputs:

• Deterministic sectors need agents that follow predefined workflows, access structured data, and use fixed tools, such as policy databases or loan calculators. 
• Nondeterministic sectors need agents that use probabilistic reasoning, real-time market data, and advanced quant finance tools, such as portfolio simulators or risk engines.

For example, if a customer asks what would happen to their portfolio if they bought gold, a multi-agent design can’t rely on an out-of-the-box LLM tool like Perplexity Finance or surface-level web research. Pulling opinions from articles or analysts isn’t enough. What’s needed is a rigorous quantitative analysis of how a gold allocation would affect the customer’s unique portfolio.

To provide the best analysis possible, the multi-agent system needs access to:

• Accurate, real-time market data
• Details of the customer’s portfolio and up-to-date risk profile
• Specialized tools like Monte Carlo simulation engines or factor risk models grounded in quantitative finance to:
  • Model market dynamics
  • Simulate portfolio performance under different conditions

A Monte Carlo simulation model – Image source: Lumina Analytica

Once you understand whether your workflows operate in a deterministic or nondeterministic context, you can begin to think about selecting the right architecture, data sources, and reasoning engines to deliver consistent, high-confidence outcomes. In the next section, we explore our approach in building these systems for financial services.

How to Approach Building Multi-Agent Systems for your FSI 

Once you decide a multi-agent system is the right choice and understand the level of complexity required, how do you start? We take a two-pronged approach that sets up boundaries before building your technical layer.

1. Start with Governance to Define What Your Agents Can Safely and Legally Do

Financial services adds more complexity than most industries and includes legacy infrastructure, fragmented tools, and strict regulatory standards like GDPR, FINRA, and regional banking supervision. It also requires the capability to scale systems across thousands of users and transactions.

This environment demands a stronger governance framework before any AI model or agent goes live. Governance sets the limits of each agent’s authority and ensures compliance, security, and traceability across every workflow.

This means defining clear rules for data, services, and reasoning.

An example of AI governance framework – Image source:  AI Governance

A well-designed governance layer prevents high-risk failures, such as data leaks, hallucinated answers, and unauthorized actions, that can quickly erode customer trust and breach regulatory standards. It also provides the foundation on which to build a reliable technical architecture (which we’ll get to in the next stage).

Here is how to build each layer of governance and why: 

1. Establish Governance over Data to Ensure Compliance and Accurate Outcomes 

Multi-agent systems rely on a range of data sources that, without the right restrictions, risk exposing sensitive client and internal information and violating regulations. Agents may also act on the wrong data, leading to inaccurate and unreliable outcomes. 

Governance defines how agents access, use, and store information, which is critical under data-specific regulations like GDPR, CCPA, and EBA Guidelines. Governance also ensures that models draw only from verified data and that each action can be traced and audited. 

Caption: An example of a data governance framework – Image source: ResearchGate

The three main data types to manage are public, private and customer-level information:

• Information from public sources like websites or open databases is typically safe. What matters most is ensuring that it’s consistently available and accessible to the agent.
• Your internal company data, including performance reports, liquidity analyses, or fund documents, must be protected from unauthorized access across departments. For instance, sales teams should not have visibility into executive or strategic planning files. 
• Customers’ personally identifiable and transactional data must be protected under strict legal and ethical limits. For example, a claims agent may need access to a customer’s record to verify eligibility, but should only be able to access the fields necessary for that decision. The system should automatically detect usage thresholds like benefit caps without exposing unrelated medical or financial details. 

When handling the data above, AI access should follow clearly defined permission levels. Each AI agent should see only the data fields required for its specific task. This level of granularity keeps data use compliant, limits exposure, and ensures that every action can be explained and audited.

Agents build on the foundation of LLMs by adding reasoning, memory, tool use, and the ability to take action in real-world systems. Image source: Alex Honchar on Medium

2. Set Up Governance over Services to Control Agent Access and Prevent Unauthorized Actions

To perform complex tasks, agents need to access services (also called tools). In financial institutions, these services include existing digital systems like payment or claims processing, policy management, customer registration, and trading. 

Service governance defines which agents can use which systems and what specific actions they can perform. Each permission must be clearly documented, monitored, and reviewed so that each agent focuses only on its assigned role. This reduces errors, prevents unauthorized actions, and ensures every action can be traced, justified, and safely reversed if needed.

For example, a payments agent may access the payments database but never the customer onboarding system. Similarly, a compliance agent might connect to transaction logs for audit purposes but cannot interact with live payment systems. This prevents cross-agent interference and potential compliance breaches or fraud, and limits the scope of damage in case of errors.

3. Create Governance over Reasoning to Guide Agent Decisions and Prevent Errors

The third layer of governance covers reasoning—how one agent routes tasks to another and decides what to do next. To understand how this fits into a multi-agent AI system, it helps to look at how each AI agent is structured and what elements shape its decisions. 

Every AI agent, powered by a language learning model (LLM), operates through three core components: 

• Data — what information it can access.
• Services — what actions it can perform.
• Reasoning — how it decides what to do next.

Reasoning connects the data and services components, guiding how agents use data and services within their defined roles and collaborate as a coordinated system. 

Typically, an orchestrator agent manages the first layer of reasoning, deciding which agent to activate next and how tasks should flow across the system. This ensures each agent stays within its scope, preventing errors and reducing the risk of unauthorized or inconsistent actions.

Internal reasoning flow in a multi-agent system

To illustrate this more clearly, let’s look at a Q&A chatbot that helps customers compare insurance policies across different banks. To do that fairly and securely, it needs multiple specialized agents:

• An orchestrator that recognizes this isn’t a simple query and routes it correctly.
• A consultant that interprets the request and triggers a search.
• A researcher that gathers competitor data and returns it.
• A cashier that manages financial operations, such as pricing or payments.
• A claims agent that processes policy changes or customer claims.

As you can see, each agent has its own role. The research agent can’t access the payment system, while the cashier agent can’t verify claims. 

This separation of logic limits risk and prevents costly mistakes, such as a research agent accidentally making a payment. It also ensures every decision can be traced and explained, reinforcing financial compliance and auditability.

Overall, when data, services, and reasoning are all strictly governed, multiple agents can collaborate safely, intelligently, and compliantly. This ensures accurate outcomes across complex financial services workflows without compromising security or trust.

2. Define the Technical Architecture of your Multiple Agents

Your technical architecture is what brings a multi-agent AI system to life. It combines software, large language models (LLMs), and infrastructure layers with the tightly governed data and services we covered earlier to form a complete, functioning system. Together, they enable how agents interact, execute tasks, and scale safely within a regulated environment. 

1. Build a Software Layer to Connect and Coordinate Agent Workflows

The software layer sets out how agents are built, connected, and orchestrated. It enables communication between sub-agents and ensures they can work together as one coordinated system within their defined roles.

This layer specifies:

• Which agents exist and what each one does
• How they exchange data or hand off tasks
• How to log and trace every action for compliance

To build these connections between agents, you can use modern frameworks like LangChain to create and manage your multi-agent system’s logic. LangChain provides general tools and libraries for connecting models, data sources, and external actions (or “tools”). It includes several sub-frameworks like LangGraph, which is designed for more specific tasks and created specifically for building multi-agent architectures. 

LangGraph structures agents as a graph, where each node represents an agent and each link defines how they interact. This makes it easier to visualize workflows and manage the flow of information across the system. Alternatively, if you’re already running AWS services, you can use AWS-native agent ecosystems, which integrate seamlessly with your existing environment.

Caption: Example of a cloud-based multi-agent AI system using AWS.

2. Create an LLM Layer to Interpret Intent and Trigger the Right Services

In multi-agent solutions, LLMs are fine-tuned to go beyond generating text and predict actions, such as when to conduct a web search, query a database, or trigger another service. They form the reasoning layer, or the “brain”, that interprets user intent and selects the right service. By managing reasoning and linking to the right data and services, LLMs create an intelligent, reliable system capable of handling complex workflows.

Key LLMs include OpenAI, Anthropic, and Gemini, or AWS Bedrock for private cloud deployments. It’s important to consider the regional availability and compliance of each model to ensure your data stays within your geographical boundaries and remains compliant with local regulations. For example:

• Anthropic models are available in Europe and Asia only through AWS, keeping data within those regions.
• OpenAI models are accessible through Microsoft Azure servers located in the UK and Sweden, ensuring data remains locally hosted.

LLM reasoning and knowledge architecture in multi-agent systems

Since LLMs in a multi-agent system are connected to verified data and deterministic services, they’re far less likely to hallucinate or produce fabricated outputs. Operating within well-governed reasoning boundaries, each agent cross-checks information through structured workflows, keeping responses accurate, auditable, and grounded in real data.

3. Design an Infrastructure Layer to Power Performance and Scalability

Infrastructure refers to the hardware that runs the software. This is the physical compute layer or the machine and cloud resources that power the LLM tools and agent orchestration, and determines how quickly and reliably your system performs under live conditions.

This layer ensures performance metrics like load management, scalability, availability, and latency. It also enables operational resilience (e.g., uptime) and compliance with regional data regulations (e.g., EU customer data is stored and processed within the EU for GDPR).

You can deploy systems on premise for maximum control or in the cloud for faster scaling. Many financial firms use hybrid approaches, which keep sensitive data and models within private environments while using cloud resources to handle variable demand. 

For instance, when user requests or model calls increase in volume, capacity can be expanded through additional compute resources. On the other hand, under lighter volumes, cloud resources can scale down automatically to control costs without compromising performance.

Your Architecture at a Glance (TL;DR)

• Infrastructure runs the system.
• Software layer coordinates agents and handoffs
• LLMs handle intent and reasoning
• Governed data and services supply trusted inputs and executable actions
• Governed reasoning routes tasks and enforces boundaries

Together, these form a predictable, auditable system that minimizes hallucinations and scales safely across complex FSI workflows.

But designing these multi-agent systems for nondeterministic environments with in-house AI development teams can be challenging. Institutions may lack the technical and architectural expertise to establish secure agent governance and access boundaries. Traditional IT and data teams in finance are often unfamiliar with systems that rely on probabilistic decision-making and dynamic reasoning. 

That’s where Neurons Lab can help. In the next section, we’ll show how we help financial institutions build custom multi-agent AI solutions capable of complex, multi-step workflows.

How to Develop Multi-Agent Systems for a Nondeterministic Sector like Wealth Management

To see how this all comes together in practice, let’s look at ARKEN, our multi-agent AI accelerator built specifically for the complexity of wealth management.

Neurons Lab’s ARKEN is a multi-agent solution accelerator for wealth management that you can customize to fit your specific use case and business KPIs

While off-the-shelf AI solutions like Claude for Finance or Perplexity Finance can surface real-time insights from public sources, they can’t interpret market dynamics or deliver accurate portfolio-level intelligence.

For wealth managers who need accurate, real-time insights to scale their workflows and free up time for more client-facing activities, these limitations are exactly what we address with ARKEN.

ARKEN’s multi-agent system generates relevant real-time insights that wealth managers can use to scale their workflows 

ARKEN sits on top of existing wealth management systems and is powered by a multi-agent framework. Its agents draw on secure public, private and customer data sources, such as live market feeds, product catalogs categorized by risk profile, client portfolios, compliance rules, and knowledge graphs, to mirror real wealth-manager decision patterns. 

Each agent then executes actions based on its defined role and action space. For example, when a wealth manager asks a question about a client’s portfolio:

• An orchestrator agent breaks it into tasks and routes them to specialized agents.
• A structured data agent gathers market insights, fund history, and performance metrics.
• An unstructured data agent analyzes client conversations, fund documents, or policy texts.
• An analytics agent runs risk models, scenario simulations, or performance evaluations.
• A synthesizing agent consolidates these outputs into an accurate, compliant, and client-specific result that reflects both market conditions and the client’s portfolio, ensuring recommendations are actionable and relevant.

The result is a multi-agent system that enhances decision-making with greater accuracy and personalization. Wealth managers can deliver faster, smarter, and more compliant client advice. 

In fact, financial institutions using ARKEN have already seen measurable business outcomes, such as: 

• 30% increase in client capacity without additional headcount.
• 2x higher client engagement touchpoints.
• 15–20% uplift in Net Promoter Score (NPS).

It’s through well-governed data, specialized agent systems, and our quantitative finance expertise that results like these are possible. 

By designing the tools and services agents rely on for financial analysis, portfolio simulation, and risk management, we ensure outputs that are as accurate and reliable as possible in real market conditions. This was the case for a global asset management firm we partnered with, where we built an AI-driven investing system and designed the quantitative tools behind it.

How a Global Asset Management Firm Built an ETF-like Investing AI Solution with Neurons Lab

A global asset management and investment advisory firm wanted to enhance its existing asset allocation approach to improve returns and reduce risk. The firm also aimed to launch a new investment product for both existing and new customers. 

Neurons Lab was tasked with developing a custom AI solution to achieve four key goals:

• Improve out-of-sample portfolio performance for more consistent and reliable results.
• Speed up strategy development, reducing the time from ideation to implementation.
• Increase risk management through advanced backtesting and scenario analysis.
• Build a scalable, secure cloud environment capable of handling large volumes of financial data and complex computations.

We built a cloud-based research platform that uses macroeconomic and fundamental stock data to better capture risk. The platform used AI-powered portfolio clustering and market structure algorithms to improve risk modelling and make market insights more accurate.

Additionally, we:

• Built an advanced backtesting framework using scenario-based and cross-validation techniques to improve confidence in out-of-sample performance.
• Deployed the solution on AWS, using Fargate for API hosting, SageMaker for real-time inference, and additional AWS services for security, monitoring, and scalability.
• Integrated several AWS services to handle LLMs for real-time inference and strategy optimization.
• Designed the architecture to ensure high availability, security, and scalability across multiple AWS availability zones. 

Neurons Lab’s scalable cloud architecture running LLMs securely with high availability across multiple zones.

As a result, the solution improved financial performance, sped up strategy development, and strengthened risk management using advanced algorithms, and a secure, scalable AWS setup. Measurable outcomes included: 

• Annual returns grew by 1%.
• Better risk control reduced drawdowns from 32% to 29%.
• The Sharpe ratio improved from 0.4 to 0.6, showing stronger risk-adjusted returns.

Why Build Custom Multi-Agent Systems with Neurons Lab

Designing and deploying a multi-agent system in financial services requires a balance of compliance, performance, and explainability across multiple domains while ensuring your system behaves predictably in both deterministic and nondeterministic environments.

Our deep understanding of both types of systems comes from years of work across insurance, wealth management and banking, allowing you to build AI that acts reliably under fixed rules or fluctuating market conditions.

As an AI-exclusive consultancy with advanced AWS competencies in Generative AI and Financial Services and a global talent network of over 500 AI engineers, Neurons Lab has delivered custom AI systems for firms including Visa, AXA, and Oschadbank.

Through a co-development approach, your teams build internal AI capability while gaining continuous access to evolving model frameworks and best practices.

This means you get accurate, traceable, and compliant multi-agent systems built to handle both rule-based and probabilistic reasoning. They scale safely across complex financial workflows and continuously adapt to regulatory and market change.

Here’s why financial services firms partner with us to develop multi-agent systems:

Get Governance by Design to Ensure Compliance and Control

Strong governance defines what each agent can access, how it reasons, and which actions it can take. Neurons Lab helps you establish clear access boundaries, granular permissions, and transparent decision logic across data, services, and reasoning layers.

This structure keeps systems compliant (e.g., with GDPR and regional banking supervision) and reduces the risk of unauthorized actions, while ensuring every decision is auditable and explainable.

Design Scalable Architecture to Speed Up Reliable AI Deployment

With frameworks such as LangChain, LangGraph, AWS Bedrock, and Azure OpenAI, your system connects software, infrastructure, and logic layers into one coordinated architecture.

Our pre-built solution accelerators, tested code, and reusable components reduce time to value and implementation risk, while hybrid-cloud options maintain performance, scalability, and regulatory compliance across markets.

Access Quantitative Intelligence to Handle Market Uncertainty

In nondeterministic domains like asset and wealth management, Neurons Lab integrates quantitative-finance tools, including factor risk models and portfolio analytics. This way, your agents can analyze portfolios and model market scenarios accurately and transparently.

Build Tailored Multi-Agent Systems with a Proven AI Partner

Building multi-agent AI systems in financial services is about creating intelligence that operates accurately, compliantly, and at scale. Achieving this requires specialized expertise in aligning governance, architecture and domain expertise. At Neurons Lab, we provide that expertise from start to finish, delivering multi-agent AI systems that work reliably and continue to evolve as market needs and regulations change.

Want to explore how a multi-agent AI system could work for your financial institution? Book a call with us today.