Building an AI Investment Research Assistant with Amazon Bedrock Multi-Agent Collaboration
Learn how to create a multi-agent AI assistant for investment research using Amazon Bedrock's multi-agent collaboration feature, enabling analysis of financial data, news, and portfolio optimization.
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
- Supervisor Agent: Orchestrates tasks, delegates to subagents, and synthesizes responses.
- 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
- A user asks, “Analyze Amazon’s financial health based on its 2024 10-K report.”
- The supervisor agent delegates:
- News Agent retrieves the report.
- Smart Summarizer extracts key metrics.
- The supervisor consolidates insights into a structured response.
Use Cases
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About the Author
David Chen
AI Startup Analyst
Senior analyst focusing on AI startup ecosystem with 11 years of venture capital and startup analysis experience. Former member of Sequoia Capital AI investment team, now independent analyst writing AI startup and investment analysis articles for Forbes, Harvard Business Review and other publications.