Amazon Bedrock Helps Strands Create Agents for Dashboard Automation

Amazon Bedrock AgentCore and Strands combined efforts to create an intelligent system for automating dashboards and reports. The solution enables AI agents to understand commands in natural language, independently extract data from various sources, transform it, and create visualizations — all without the involvement of an analyst or data engineer.

Solution Architecture

The system is built on three key components. Amazon Bedrock AgentCore provides the foundation for creating and managing agents — they are capable of breaking down a complex user task into subtasks, calling the necessary AWS services, processing results, and coordinating work. Strands Agents handles logic and orchestration — managing data flow between components, synchronization, and execution control. Amazon QuickSight Transforms processes and transforms data into the required format, creating interactive dashboards, charts, and tables.

A typical workflow scenario: a user (manager, analyst, executive) speaks or writes in a chat request: "show revenue dynamics by region for the last quarter" or "which products are declining in sales by more than 20 percent". The agent does this:

• Parses natural language and determines what data and metrics are needed
• Accesses sources — Amazon S3, RDS, Redshift, DataLake
• Applies filters, groupings and transforms data according to the request
• Creates charts, tables and interactive dashboards with refined parameters
• Returns a ready result with conclusions and recommendations

All of this happens within seconds, without writing SQL queries or configuring BI tools.

Security and Production Readiness

AWS particularly emphasizes that the solution was developed in compliance with corporate security standards. Data is encrypted both in transit and at rest on servers. Access is controlled granularly through IAM policies and roles, all agent actions are logged and preserved for audit and compliance. Agents execute in a fully isolated environment, which eliminates unauthorized access to sensitive information from other segments of the system. Scalability is built into the architecture — the system works equally efficiently with data from young startups (gigabytes) or multi-petabyte data lakes of large corporations (petabytes).

"This is the first serious step toward fully autonomous business

analytics in a corporate environment," describe the capabilities of the solution its authors.

Who This Is Relevant For

The solution is addressed to analysts, managers, BI specialists, financial teams, and operational leaders who spend dozens of hours per week creating reports and analyzing data manually. The traditional workflow requires complex interaction between the user (business, posing the question) and the technical specialist (SQL engineer, BI developer, executing the query). With an AI agent, this chain is radically shortened — the user formulates the question in chat themselves, and the system automatically finds the answer and prepares visualization. Particularly useful for large organizations where data is stored in different systems (ERP, CRM, warehouse, data lakes), and a traditional analyst must figure out where to look for information and how to combine it themselves.

What This Means

AI agents are moving from academia and research laboratories into real business production. This means that part of the work of data specialists and analysts will be gradually automated. For companies, this provides acceleration of the analytics cycle, reduction of manual labor costs, and faster decision-making cycles — instead of a day or two for a report, an answer in minutes. For the labor market, this is a signal: demand for junior analysts and reporting specialists is declining, while demand for specialists who can work with agents, manage them, and integrate them into business processes is growing.