How to Build a Multi-Step Financial Analyst Bot in Amazon Q Business with Agentic RAG

The fluorescent lights of the office hummed, a monotonous soundtrack to the frantic clicking of Sarah’s keyboard. As a senior financial analyst, she was no stranger to pressure, but this quarterly earnings report was a behemoth. Piles of SEC filings, terabytes of market data, endless analyst commentary, and the transcript from the investor call all needed to be digested, cross-referenced, and synthesized into a coherent narrative for the board. Her current AI assistant, built on a standard Retrieval-Augmented Generation (RAG) framework, was good at fetching facts. She could ask, “What was the total revenue for Q3?” and get a number. But that wasn’t the real work. The real work was the multi-step reasoning process: finding the revenue, comparing it to Q2, analyzing the sentiment in the investor call about that revenue, and summarizing the key drivers behind the change. Her current system couldn’t connect those dots; it could only answer one question at a time, leaving her to manually stitch the insights together.

This gap between simple data retrieval and complex, sequential task execution is the single biggest challenge holding back enterprise AI from true automation. We don’t just need AI that can find information; we need AI that can act on it, that can reason through a problem, and that can execute a plan. The solution lies in a groundbreaking evolution of RAG, a paradigm shift that AWS is pioneering. It’s called “agentic RAG,” and it’s the core intelligence behind the latest updates to Amazon Q Business. This isn’t just another incremental update; it’s a re-imagining of what an enterprise AI can be. Instead of a simple Q&A bot, you can now build a sophisticated digital colleague—an agent capable of decomposing a complex request like Sarah’s into a series of logical steps and executing them autonomously.

In this technical walkthrough, we’ll move beyond theory and get our hands dirty. We will build the exact financial analyst bot that Sarah needs. You will learn, step-by-step, how to leverage the new agentic RAG capabilities within Amazon Q Business to create a system that automates complex financial analysis. We’ll set up the environment, connect data sources, and, most importantly, configure the agentic logic that allows for multi-step reasoning. To cap it off, we’ll give our bot a professional voice, turning its final text-based analysis into a clear, audible executive summary using ElevenLabs. Prepare to build not just a tool, but a true AI teammate.

Understanding the Leap: From Standard RAG to Agentic RAG

To appreciate the innovation of agentic RAG, we must first understand the ceiling of its predecessor. The traditional RAG model has been a workhorse for enterprise AI, but its architecture is fundamentally limited.

The Limitations of Traditional RAG

A standard RAG system operates on a linear, single-turn process: query, retrieve, generate. You ask a question, the system retrieves relevant documents from a vector database, and then the Large Language Model (LLM) uses that context to generate an answer. This is incredibly effective for fact-based lookups and simple summarizations.

However, it fails when faced with a query that requires multiple steps or tools. For Sarah’s task—analyzing a quarterly report—a traditional system would require her to issue a sequence of separate commands, manually piecing together the final report. The system lacks the executive function to create and follow a plan.

Introducing the “Agentic” Shift with Amazon Q Business

Agentic RAG, as implemented in Amazon Q Business, transforms the LLM from a simple generator into a reasoning engine—an agent. As detailed in the AWS announcement on August 14, 2025, this new approach allows the system to autonomously decompose complex user requests into smaller, manageable sub-tasks. It can then orchestrate a variety of tools, including the built-in retriever, to execute these tasks in a logical sequence.

Think of it as the difference between a librarian who can find any book you ask for and a research assistant who you can ask to write a paper on a topic. The research assistant will find the books, read them, synthesize the information, and produce a new document. That’s the power of agentic RAG.

Why This Matters for Financial Analysis

The impact on a workflow like financial analysis is transformative. An agent can take a high-level request and execute the entire research process. According to industry analysis, financial professionals can spend up to 50% of their time gathering and processing data rather than on high-value analysis. An agentic system directly targets this inefficiency, potentially slashing research time and freeing up analysts to focus on strategic insights and client relationships.

Setting Up Your Environment in Amazon Q Business

Before we can build our agent, we need to prepare the workspace within AWS. This involves creating our Amazon Q application and feeding it the necessary data to become an expert.

Prerequisites

To follow along, you will need:

• An active AWS account with appropriate permissions.
• Access to the Amazon Q Business service.
• A sample set of documents to serve as your knowledge base. For our use case, this would include PDF versions of public 10-K filings, transcripts from earnings calls, and saved HTML files of relevant market news.

Creating Your Amazon Q Application

First, we’ll set up the container for our AI analyst. The process is straightforward:

1. Navigate to the Amazon Q Business console in AWS.
2. Click on “Create application.”
3. Name your application (e.g., FinancialAnalystBot) and provide a brief description.
4. Follow the prompts to create a new service role or use an existing one, granting Q the necessary permissions to access other AWS services.
5. Select a retriever. For this use case, start with the native retriever, which is optimized for unstructured documents.

Connecting Your Data Sources

An agent is only as smart as the data it can access. Connecting your data sources is a critical step:

1. Inside your newly created application, navigate to the “Data sources” tab and click “Add data source.”
2. Choose the appropriate connector. For our PDFs and text files, the Amazon S3 connector is ideal. You can point it to an S3 bucket where you’ve uploaded your financial documents.
3. Configure the sync settings. You can set it to run on a schedule, ensuring your bot always has the latest information as new filings and reports are published.
4. Once connected, Amazon Q will begin indexing your documents, converting them into searchable embeddings for the RAG process.

Building the Agentic Logic: A Step-by-Step Implementation

With the foundation laid, we can now construct the agentic brain of our financial analyst bot. This is where we define its capabilities and guide its reasoning process.

Defining the Agent’s Tools

An agent uses “tools” to accomplish tasks. In Amazon Q Business, the retriever for your connected data sources is a default tool. However, you can enhance its capabilities significantly. For instance, you could add a tool that connects to a stock market API to fetch real-time data or a code interpreter tool that can perform complex calculations on data it retrieves.

Crafting the Master Prompt

The master prompt, or instruction, is the agent’s job description. A well-crafted prompt is essential for guiding its behavior. Here’s an example for our FinancialAnalystBot:

“You are a world-class financial analyst AI. Your purpose is to assist users by conducting detailed, multi-step analysis on financial documents. When given a complex query, break it down into a logical sequence of sub-tasks. Use your available tools to find information, perform comparisons, analyze sentiment, and synthesize the results into a clear, concise summary. Present your findings in a structured format.”

This prompt sets the persona, defines the core function (multi-step analysis), and instructs the agent on how to behave.

Enabling Multi-Step Task Decomposition

This is the magic of agentic RAG. When a user submits a complex query, the agent—guided by the master prompt—engages in a reasoning process. Let’s use our original query:

“Analyze our company’s last quarterly earnings report. Compare revenue to the previous quarter, summarize key takeaways from the earnings call transcript, and identify the top three market concerns mentioned in recent news articles.”

Amazon Q’s LLM will automatically break this down into a plan, which might look something like this:
1. Task 1: Search the document index for the latest quarterly earnings report and identify the total revenue figure.
2. Task 2: Search the index for the previous quarter’s report and identify its total revenue figure.
3. Task 3: Compare the figures from Task 1 and Task 2 to calculate the change.
4. Task 4: Search the earnings call transcript for sections discussing revenue and summarize the key points.
5. Task 5: Search the connected news articles for mentions of the company and identify recurring negative themes or concerns.
6. Task 6: Synthesize the findings from all previous tasks into a final, structured report.

The agent executes this plan, self-correcting if a step fails, and presents the final, unified answer to the user.

Bringing Your Bot to Life with a Realistic Voice

A comprehensive text report is powerful, but transforming it into a human-like audio summary makes it more accessible and impactful, especially for busy executives.

Why Voice Output Enhances the User Experience

Adding a voice turns your bot from a passive tool into an active assistant. An executive can simply ask for an audio briefing during their commute. It enhances accessibility and adds a layer of professionalism that text alone cannot match. This final touch is what separates a good tool from a great one.

Integrating ElevenLabs for High-Fidelity Audio

ElevenLabs is a leader in AI-powered text-to-speech, known for its incredibly realistic and emotive voices. Integrating it with our agent’s output is simple using their API.

Here’s a basic Python script showing how you would pass the final text from Amazon Q to ElevenLabs:

import requests

# Your ElevenLabs API Key
ELEVENLABS_API_KEY = "YOUR_API_KEY_HERE"

# The final summarized text from your Amazon Q agent
agent_output_text = "... your bot's detailed financial summary ..."

# ElevenLabs API endpoint
url = "https://api.elevenlabs.io/v1/text-to-speech/YOUR_VOICE_ID"

headers = {
  "Accept": "audio/mpeg",
  "Content-Type": "application/json",
  "xi-api-key": ELEVENLABS_API_KEY
}

data = {
  "text": agent_output_text,
  "model_id": "eleven_multilingual_v2",
  "voice_settings": {
    "stability": 0.5,
    "similarity_boost": 0.5
  }
}

response = requests.post(url, json=data, headers=headers)

# Save the audio file
with open('financial_summary.mp3', 'wb') as f:
    f.write(response.content)

Choosing the Right Voice

With ElevenLabs, you can select a pre-made professional voice from their extensive library or even use their voice cloning technology to create a custom, branded voice for your company’s AI assistant. A calm, authoritative voice can instill confidence and make the data more compelling.

Remember Sarah, our analyst buried under a mountain of reports? With the agentic RAG system we just designed, her workflow is revolutionized. She now directs a powerful AI assistant that handles the painstaking research, and with the final touch of a voice integration, it can even deliver the initial briefing for her. The journey from a simple fact-checker to a sophisticated digital colleague is now complete.

Building this potent tool is just the beginning. The quality of the final verbal summary depends entirely on the quality of the voice. To give your financial analyst bot the authoritative and professional voice it deserves, you need a best-in-class text-to-speech engine. Bring your financial analyst to life with a realistic voice from ElevenLabs. Click here to sign up and transform your data into compelling audio insights.