Building a Data Analyst Agent with IBM Watsonx Orchestrate
Manual data cleaning, analysis, and reporting can slow down business teams and lead to missed opportunities. Imagine a virtual agent that analyses your data, generates meaningful insights, and tells a compelling story—automatically and on demand. Thanks to agentic AI capabilities in IBM Watsonx Orchestrate, this is now possible. In this blog, discover how to build a smart data analyst workflow that turns ordinary spreadsheets into actionable business knowledge.
Why Automate Data Analysis?
Data analysts often spend hours on repetitive tasks:
Automation via a data analyst agent accelerates these steps and lets teams focus on driving business value.
Step 1: Set Up Your Agent
Begin by selecting “Create from Scratch” option in Watson Orchestrate. Assign your agent a name such as “DataAnalyst_Agent” and a concise description outlining the agent’s purpose.
Once created, you’ll arrive at the agent profile page. Here, you can fine-tune capabilities and select a reasoning style like ReAct for flexible, stepwise problem-solving.
To learn more about ReAct AI systems refer : https://www.ibm.com/think/topics/react-agent
Step 2: Add Your Knowledge Sources
Enhance your agent’s intelligence by uploading:
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A sample CSV file containing structured, representative business data.
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A PDF of an IPYNB file featuring exploratory data analysis (EDA) and answers to business questions.
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A business report summarising insights from the same dataset
Click on “Choose Knowledge” button and Select “Upload Files“ to upload the sample CSV Files and pdfs.
By supplying both raw data and analytical context in the “Knowledge Source Description” the agent can take inspiration from these content to deliver informed, relevant responses tailored to user queries.
Step 3: Create Agent Flow
Navigate to the Toolset option. On clicking “Add a New Tool” button, a popup appears with several options—select “Create an Agentic WorkFlow” to build your custom workflow.
Add the key workflow components:
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Business Context: Input describing the analytical scenario or problem to solve.
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Sample Dataset: File upload activity, allowing users to provide relevant dataset for the business problem.
Start by adding a user activity for business context.Also, in the same User Activity, select File upload option to input the dataset. Rename the workflow and click “Done” to save the workflow.
Step 4: Define Agent Behaviour and Guidelines
Specify clear behavioral instructions for your agent, such as:
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Clean data, identify trends, and present insights using data storytelling principles.
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Respond concisely and accurately, tailored for both technical and business audiences.
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Always cite analytical sources and data used.
Add guidelines to reinforce best practices, including:
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Use only the provided knowledge sources for analysis.
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Note data limitations and avoid unsupported conclusions.
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Respect business privacy and confidentiality.
Step 5 : Preview the Agent
Before deploying your agent publicly, preview the workflow. Provide a short and suitable invocation phrase and clear prompts for the agent to accept feedback and perform requested actions.
Acknowledgement:
I’d like to thank Dennis Parrot, Emily Hagopian and Justin Wang for their insights and support during the development of this blog. Their expertise helped to shape the direction and depth of the article.
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