OPLOG разработала три BI-агента на Amazon Bedrock с Claude Sonnet

OPLOG developed three AI agents for automating business analytics tasks using Strands Agents SDK and Amazon Bedrock AgentCore. The project demonstrates how integrating Claude Sonnet with RAG systems transforms the approach to enterprise analytics and information processing.

Three Specialized Agents

OPLOG created three separate AI agents, each solving a specific class of tasks. The first agent handles data collection and structuring from various sources. The second focuses on analysis and pattern discovery. The third generates analytical reports and recommendations. Such specialization allows each agent to become an expert in its domain, rather than attempting to create a universal solution. The agents function as virtual assistants for analysts — they can independently search for information in corporate systems and prepare substantiated recommendations.

Amazon Bedrock AgentCore as Platform

The deployment of three agents occurred on Amazon Bedrock AgentCore — a managed AWS service specifically designed for running and scaling AI agents. Choosing this platform allowed OPLOG to focus on developing agent logic without getting distracted by infrastructure concerns. Bedrock AgentCore handles all complexities: request processing, memory management, and integration with other AWS services. The company used Strands Agents SDK — a tool that simplifies the process of creating, testing, and deploying agents. The SDK provides ready-made templates and functions, accelerating development. Through this approach, OPLOG was able to quickly launch three fully functional agents into production.

Integrating Claude Sonnet and RAG

The core of the solution is the integration of Claude Sonnet with Amazon Bedrock Knowledge Bases for Retrieval Augmented Generation (RAG). Claude Sonnet serves as the "brain" of the agents, but instead of relying solely on the model's built-in knowledge, the agents use RAG — a technique for searching relevant information in corporate databases before responding. How it works in practice:

• A user asks a question or describes an analytics task
• The RAG system searches for relevant documents and data in Knowledge Bases
• Found information is sent to Claude Sonnet's context
• The model generates a response based on its own knowledge plus corporate data
• The response includes source references for verification

The advantages of this approach are obvious. First, high accuracy — the agent does not hallucinate but relies on actual company data. Second, complete traceability — every response can be verified against sources. Third, scalability — Amazon Bedrock Knowledge Bases integrates with various data stores: relational databases, document repositories, API services, and cloud storage.

What This Means for Analytics

This OPLOG case demonstrates the standard for enterprise AI. Instead of universal chatbots, companies build specialized agents integrated with their own data and processes. Analysts get assistants that work at expert speed 24/7 without attention errors. The combination of a powerful model (Claude Sonnet), a reliable platform (Bedrock AgentCore), and proper architecture (RAG + specialization) becomes a hallmark of a mature approach to enterprise AI.