Building an AI Investment Research Assistant with Amazon Bedrock Multi-Agent Collaboration

Financial analysts face significant challenges in processing diverse data types—structured data (like stock prices), unstructured text (such as SEC filings), and multimedia content (earnings calls). Traditional workflows are inefficient, and delays can lead to missed opportunities or overlooked risks. AI-powered assistants can automate routine tasks, but a single AI agent often struggles with complex, multi-step research workflows.

The Multi-Agent Solution

Amazon Bedrock’s multi-agent collaboration capability addresses this by enabling specialized AI subagents to work together under a supervisor agent, mimicking real-world research teams. Key benefits include:

• Distributed problem-solving: Each subagent specializes in a specific task.
• Improved accuracy: Expertise is tailored to data types (quantitative analysis, news retrieval, summarization).
• Scalability: New agents can be added without rebuilding the entire system.
• Transparency: Each agent’s reasoning is traceable.

Key Components

1. Supervisor Agent: Orchestrates tasks, delegates to subagents, and synthesizes responses.
2. Subagents:
   • Quantitative Analysis Agent: Handles stock data queries and portfolio optimization.
   • News Agent: Retrieves financial reports and news via web search or knowledge bases.
   • Smart Summarizer Agent: Condenses lengthy documents into actionable insights.

Technical Implementation

The architecture leverages:

• Amazon Bedrock Agents: For agent creation and orchestration.
• Amazon Bedrock Data Automation (BDA): Processes unstructured data (documents, audio, video) for RAG workflows.
• Amazon Nova LLMs: Powers supervisor and subagents.
• AWS Lambda & OpenSearch Serverless: For data processing and retrieval.

Example Workflow

1. A user asks, “Analyze Amazon’s financial health based on its 2024 10-K report.”
2. The supervisor agent delegates:
   • News Agent retrieves the report.
   • Smart Summarizer extracts key metrics.
3. The supervisor consolidates insights into a structured response.

Use Cases

• Stock Performance Analysis: Correlate price movements with news events.
• Portfolio Optimization: Allocate assets based on historical data.
• Financial Health Reports: Summarize earnings calls and filings.

Getting Started

• GitHub Repo: Investment Research Assistant
• Workshop: Amazon Bedrock Multi-Agent Collaboration

Conclusion

This solution demonstrates how multi-agent AI systems can transform investment research by combining specialized expertise with seamless orchestration. Financial institutions can deploy similar frameworks to enhance decision-making while maintaining transparency and scalability.

Results are demonstrative and not financial advice.