Financial markets move faster than a caffeinated day trader on earnings announcement day. Traditional financial analysis tools? This becomes especially true when you process massive amounts of data simultaneously. Add generating investment insights to the mix. Then coordinate complex workflows on top of that. Stepping up to meet this challenge are AI-driven financial assistants. Built using autonomous AI agents, they can create customized investment portfolios and conduct in-depth company research using sources like FOMC reports. They can also handle communications by generating automated financial summaries and stakeholder-ready reports.

Unlike single-purpose AI tools that handle one task at a time, autonomous AI agents orchestrate multiple financial operations smoothly. They make independent decisions based on market conditions while collaborating with specialized agents to solve complex problems. Think of them as your financial dream team, where each agent brings specific expertise, yet they all work together flawlessly.

Amazon Bedrock’s multi-agent collaboration framework is transforming financial solution development. Instead of relying on monolithic systems, you can deploy specialized agents that each excel in their domain. Together, they operate in seamless coordination to manage complex financial workflows.

Curious how multi-agent orchestration works in practice? Check out our blog on Simplifying Complex Tasks with Multi-Agent Collaboration to see this architecture in action.

In this comprehensive guide, we’ll show you how to build a sophisticated AI financial assistant using Amazon Bedrock agents. The system will combine portfolio creation, market research, and automated reporting into a cohesive, intelligent workflow. By the end, you’ll have a production-ready solution that even Gordon Gekko would envy.

Challenges in Building a Sophisticated Financial Assistant

Before diving into the solution, let’s acknowledge the elephant in the trading room. Building effective financial AI systems often feels overwhelming, like solving a Rubik’s cube while riding a unicycle. Technically possible, but unnecessarily complicated.

Data Complexity Overload

Financial data arrives in many forms, from real-time market feeds and daily regulatory documents to quarterly company filings and monthly economic indicators. On top of that, unstructured research reports accumulate continuously, adding to the complexity that traditional systems struggle to manage.

Complex Workflow Orchestration

Financial processes rarely follow a linear path, often forming complex dependency chains that traditional systems struggle to manage. For instance, portfolio creation may trigger compliance checks, which then require additional research before culminating in stakeholder communications.

Scalability Bottlenecks

Single-agent systems can’t scale with growing operations. While they may function initially, their performance quickly declines under increased demand. This leads to mounting operational inefficiencies that worsen over time and hinder overall system reliability.

Integration Challenges

Financial institutions typically manage dozens of systems, multiple APIs, and complex database relationships simultaneously. Getting them to play nicely together requires significant technical expertise, often creating unstable system connections.

Why Autonomous AI Agents on Amazon Bedrock Are the Solution

The financial industry has experimented with everything from rules-based systems to traditional machine learning models. Yet these approaches consistently fall short when it comes to managing today’s dynamic workflows. Modern financial processes are deeply interconnected and inherently complex, and require more adaptive solutions.

Autonomous AI agents on Amazon Bedrock represent a true paradigm shift in financial technology. Instead of relying on rigid systems that collapse under complexity, we can now build intelligent agents designed to adapt in real time. These agents collaborate fluidly across tasks and scale effortlessly with your evolving business needs.

Amazon Bedrock’s multi-agent framework doesn’t just address technical challenges. It fundamentally changes how financial institutions approach AI deployment. Instead of spending valuable time managing infrastructure, you can focus on building agents that deliver actionable insights and automate complex workflows with ease.

Discover how Bedrock Agents shift from passive AI to action-oriented workflows in How Amazon Bedrock Agents Are Revolutionizing Automation.

AI agents on Amazon Bedrock are far more than conversational tools. They’re strategic decision-makers. Think of them as a team of hedge fund analysts merged with trading algorithms, powered by foundation models such as Claude 4.

These agents bring four key superpowers to the table:

• Task Decomposition & Orchestration: They break down complex prompts like “build a tech portfolio” into manageable phases: research, analysis, selection, and reporting. Each phase is executed systematically.
• Real-Time Integration: APIs, internal systems, and third-party data sources connect effortlessly. This allows agents to access up-to-the-minute market feeds, company filings, and regulatory updates.
• Intelligent Memory and Reasoning: Agents maintain memory across sessions and make decisions using past context, learned patterns, and real-time data. The result is a more personalized, human-like analysis.
• Knowledge Base and Code Execution: Using RAG workflows and built-in code execution, they convert documents into insights, generate reports, perform calculations, and visualize trends.

Together, these capabilities allow your financial assistant to operate autonomously, coordinate with specialized agents, and adapt dynamically to evolving conditions without human micromanagement.

Architecture of an Agentic AI Financial Assistant on Amazon Bedrock

Now that we’ve established why autonomous AI agents are the future of financial technology, let’s examine the architectural blueprint that makes it all possible.

In high-stakes environments like investment management, “technically adequate” simply isn’t enough. Financial systems demand precision and reliability, and that’s exactly where task specialization is essential.

Amazon Bedrock’s multi-agent framework enables this by assigning specialized tasks to dedicated agents. Rather than overloading a single agent, you deploy a coordinated team, each designed to perform a specific function with precision.

This distributed approach delivers several advantages:

• Enhanced accuracy through specialized domain expertise
• Parallel processing capabilities that dramatically reduce analysis time
• Modular architecture that makes updates and maintenance pleasant
• Scalability that grows with your business needs naturally

Designing a Multi-Agent System on Amazon Bedrock

The orchestration magic happens through two primary modes that provide flexibility for different operational scenarios:

• Supervisor Mode: Your central agent receives complex requests, analyzes them systematically, and then delegates specific tasks to specialized agents. Think of it as having a brilliant project manager who knows exactly which team member should handle each aspect of financial analysis, ensuring perfect coordination throughout the process.
• Router and Supervisor Mode: This hybrid approach offers intelligent routing where the system first attempts to route simple requests directly to appropriate specialists. Complex scenarios automatically escalate to full supervisory coordination, ensuring efficient processing while maintaining thorough analysis when needed.

Our AI financial assistant consists of three specialized agents.

Figure 1: Multi-Agent System for a Financial Assistant

Financial Supervisor Agent

This central agent handles user interactions smoothly while coordinating task delegation efficiently and ensuring all components work together seamlessly. It serves as your single point of contact, keeping complex multi-agent coordination hidden from users.

Portfolio Assistant Agent

Your dedicated Bedrock portfolio creation agent specializes in several areas:

• Creating customized investment portfolios based on specific criteria
• Managing stakeholder communications through automated email systems
• Generating detailed financial reports and comprehensive summaries
• Handling portfolio optimization and rebalancing recommendations

Data Assistant Agent

The research powerhouse serves as your knowledge repository:

• Processing and analyzing FOMC documents, earnings reports, and market data
• Providing data-driven insights on economic trends and company performance
• Integrating with Amazon Bedrock Knowledge Bases for intelligent document retrieval
• Maintaining audit trails for compliance and decision tracking

This architecture delivers the optimal combination for financial AI: specialized expertise with seamless coordination. The result is a system that is both powerful and manageable.

Building Your AI Financial Assistant: A Step-by-Step Guide

Now it’s time to turn architecture into action. In this section, we’ll walk through how to build an AI financial assistant using Amazon Bedrock that can automate portfolio management, analyze market data, and communicate with stakeholders. You’ll learn how to structure agent workflows, integrate real-time data sources, and deploy a production-ready solution tailored to financial operations.

Prerequisites and Setup

Before building our Amazon Bedrock financial assistant, ensure your development environment meets these requirements:

• AWS Account Configuration: Choose your preferred region (we recommend us-west-2 for optimal Amazon Bedrock performance) and ensure you have appropriate permissions for Amazon Bedrock, AWS Lambda, Amazon S3, and Amazon SES services.
• Model Access Setup: Enable access to Amazon Titan Embeddings and Amazon Nova Lite models through the Bedrock console. These models power your agent’s reasoning capabilities and text processing requirements.
• Storage Infrastructure: Create an S3 bucket using the naming convention financial-data-[your-org] for storing financial documents, research reports, and system artifacts.
• Security Considerations: Set up appropriate IAM roles and policies to ensure your agents have necessary permissions while maintaining security best practices for production deployments.

Phase 1: Developing the Data Assistant Agent

This agent handles financial document ingestion, regulatory analysis, and knowledge retrieval. You can follow the steps below to build and configure it effectively.

Step 1: Create the Knowledge Base

• Begin by initiating the knowledge base creation process in Amazon Bedrock. Follow the official instructions in the Create a Knowledge Base documentation.
• For the data source configuration, select Amazon S3 as your primary source and choose the appropriate S3 bucket containing your financial documents (e.g., FOMC reports, earnings summaries, and market research).
• Initiate synchronization by connecting to your S3 source.
• For the embedding model, choose Amazon Titan Embeddings – Text, and keep all other parameters set to their default values.
• Carefully review your configuration on the summary page. When ready, click Next to finalize the creation.
• Be sure to note the name of your knowledge base for later reference.
• Wait for the build process to complete before moving forward (this may take several minutes).

Step 2: Create the Knowledge Base Agent

Once your knowledge base is ready, manually create a Knowledge Base Agent by following the Create and configure agent manually steps in the Amazon Bedrock documentation.

• During agent creation, use the following instruction prompt:
  • Utilize this knowledge base when responding to financial data queries.Focus on economic trends, company financial statements, and FOMC outcomes.Include S&P 500 and NASDAQ indices in your analysis.Limit responses strictly to knowledge base content
  • Assist in agent orchestration for data provision.
• In the Knowledge Base section of the agent configuration, click Add, and select the previously created knowledge base from the dropdown list.
• Maintain default settings for all remaining configuration options unless specific adjustments are required.

Phase 2: Building the Portfolio Assistant Agent

Your portfolio assistant agent requires more sophisticated capabilities, including external integrations that enable real-world operations.

Step 1: Develop Lambda Function

Create an AWS Lambda function that serves as the backend for portfolio operations. This function handles company research, portfolio creation logic, and email communications.

• Configure Python 3.12 as the runtime environment.
• Set up function schema to respond to agent invocations.
• Implement backend processing capabilities for portfolio creation operations.
• Integrate the implementation code for proper functionality with the Amazon Bedrock agent system.

The Lambda implementation includes three core functions:

• companyResearch(): Retrieves detailed financial data for specific companies.
• createPortfolio(): Generates optimized portfolios based on user criteria.
• sendEmail(): Manages stakeholder communications with portfolio summaries.

Step 2: Configure Action Group Schema

Define a comprehensive API schema that enables your agent to interact with the AWS Lambda function. Use the following OpenAPI 3.0.1 schema for your action group configuration:

• The schema should include endpoints for /companyResearch, /createPortfolio, and /sendEmail.
• Configure proper parameter validation for company names, portfolio criteria, and email addresses.
• Set up response schemas that define expected data structures for portfolio and company data.

Step 3: Configure Agent Instructions

Add the following instruction set to your portfolio assistant agent:

You are an investment analyst. Your job is to assist in investment analysis,
create research summaries, generate profitable company portfolios, and facilitate
communication through emails. Here is how I want you to think step by step:

1. Portfolio Creation:
   Analyze the user’s request to extract key information such as the desired
   number of companies and industry.
   Based on the criteria from the request, create a portfolio of companies.
   Use the template provided to format the portfolio.

2. Company Research and Document Summarization:
   For each company in the portfolio, conduct detailed research to gather relevant
   financial and operational data.
   When a document, like the FOMC report, is mentioned, retrieve the document
   and provide a concise summary.

3. Email Communication:
   Using the email template provided, format an email that includes the newly created
   company portfolio and any summaries of important documents.
   Utilize the provided tools to send an email upon request, including a summary
   of provided responses and portfolios created.

Step 4: Configure Email Integration

Configure Amazon SES to handle automated reporting and stakeholder communications:

• Set up Amazon SES in your AWS account following the Send an Email with Amazon SES documentation.
• Configure verified email addresses for sending communications.
• Update the Lambda function with appropriate SES permissions and email templates.

Phase 3: Developing the Financial Supervisor Agent

The supervisor agent serves as the orchestration layer for your Autonomous AI Agents system, coordinating all operations between specialized agents.

Step 1: Create the Supervisor Agent

Create your supervisor agent following the Create and configure agent manually steps. Configure the agent with coordination-focused instructions:

• You are an investment analyst coordinator.
• Assist in investment analysis by delegating tasks to specialized agents.
• Coordinate their responses systematically.
• Provide comprehensive financial insights.
• Collaborate with specialized agents effectively.
• Deliver the desired output based on the retrieved context.

Step 2: Configure Multi-Agent Collaboration

Configure the supervisor agent to work with both the portfolio assistant and data assistant agents:

• In the multi-agent collaboration section, choose Edit.
• Add the knowledge base agent as a supervisor-only collaborator, without including routing configurations.
• Add the portfolio assistant agent with both routing and supervision capabilities.
• Set up collaboration to handle varying complexity levels in user requests.

Step 3: Advanced Prompt Engineering

Implement sophisticated prompt templates in the Advanced prompts section to guide the supervisor agent through complex financial workflows:

• Add portfolio examples and email formatting templates.
• Configure chain of thought prompting (CoT) techniques to control agent behavior.
• Ensure the agent follows your designed orchestration pattern while minimizing hallucination.

Best Practices for Implementation of Multi-Agent Systems

These technical implementation patterns represent industry best practices for deploying Amazon Bedrock multi-agent systems in production financial environments:

• Action Groups with Validation: Define action groups that handle portfolio operations with comprehensive input validation and error handling. This approach ensures data integrity, prevents malformed requests from causing system failures, and maintains audit trails required for financial compliance. Best practice includes implementing parameter validation, request sanitization, and proper error responses.
• Knowledge Base Integration: Enable intelligent document retrieval that follows established patterns for financial data management. This includes implementing proper document versioning, maintaining data lineage for regulatory compliance, and ensuring secure access controls. Industry best practice requires automated document ingestion, semantic search optimization, and regular knowledge base updates to maintain data freshness.
• API Orchestration: Coordinate response handling across multiple agents using established microservices patterns. This includes implementing proper service mesh communication, maintaining consistent data formats across agent boundaries, and ensuring graceful degradation when individual agents are unavailable. Best practice dictates implementing circuit breakers, retry logic, and comprehensive logging for troubleshooting.
• Monitoring and Observability: Implement comprehensive monitoring that follows observability best practices for distributed systems. This includes tracking agent performance metrics, monitoring response times, implementing distributed tracing across agent interactions, and setting up alerting for system anomalies. Production best practice requires implementing health checks, performance benchmarking, and automated scaling based on demand patterns.

Real-World Applications for AI Agents in Financial Services

Autonomous AI agents are no longer just experimental tools; they’re becoming essential for financial institutions seeking speed, precision, and scalability. Here’s how AI agents are being deployed to deliver measurable value in real-world scenarios.

Investment Research and Analysis

• Multi-Modal Intelligence: AI agents simultaneously ingest and analyze diverse data types such as earnings reports, call transcripts, market news, and structured financial data. This enables rapid, holistic investment analysis that traditionally took analysts days or weeks to compile.
• Automated Research Reports: Agents generate professional-grade investment reports that include market trends, company evaluations, and tailored portfolio recommendations. Output is consistently formatted to match organizational standards, reducing manual work and improving turnaround time.

Risk Assessment and Compliance

• Real-Time Compliance Monitoring: You can deploy specialized agents that track regulatory updates and assess portfolio alignment with compliance frameworks continuously. They flag issues proactively before they escalate into violations or fines.
• Anomaly & Fraud Detection: With advanced pattern recognition capabilities, AI agents identify suspicious trading activity and account behavior in real time, enabling institutions to mitigate fraud swiftly and effectively.

Portfolio Management and Optimization

• Dynamic Portfolio Construction: Using parameters like risk appetite, sector preferences, and investment horizon, agents create optimized portfolios that balance returns and risk using real-time market data.
• Automated Rebalancing & Insights: Agents monitor performance and suggest rebalancing strategies based on predefined models and current market movements, ensuring portfolios stay aligned with client goals.
• Stakeholder Reporting: Automatically generate performance summaries, market updates, and personalized recommendations, helping advisors communicate with clients consistently and efficiently.

Operational Efficiency and Cost Savings

• Drastic Reduction in Manual Work: Tasks that took analysts hours can now be completed in minutes. This frees up talent to focus on strategy rather than data wrangling.
• Higher Accuracy & Consistency: Automation minimizes human error, ensuring every report and recommendation meets a consistent quality standard.
• Built-In Scalability: As your data and client base grow, AI agents scale effortlessly, without the need to increase human resources.

Accelerate Agentic AI Adoption in Financial Services with Cloudelligent

As an AWS Advanced Consulting Partner, Cloudelligent specializes in building the Amazon Bedrock multi-agent systems. Our certified experts help financial institutions accelerate implementation, optimize agent performance, scale across operations, integrate with existing systems, and ensure regulatory compliance.

Book your free consultation and discover deployment strategies. Let’s turn your financial AI assistant from concept to competitive advantage.