Introduction to Amazon Bedrock Agent

Overview of Amazon Bedrock

Amazon Bedrock is a fully managed service that enables businesses to easily and efficiently access and use high-performance Foundation Models (FMs).

Introduction to Amazon Bedrock Agent

Amazon Bedrock Agent enables Generative AI applications to perform multi-step tasks by connecting to enterprise systems, APIs, and data sources.

The Agent leverages the reasoning capabilities of Foundation Models (FMs) combined with APIs and data to analyze user requests, retrieve necessary information, and complete tasks efficiently.

Architecture Analysis of Amazon Bedrock Agent with Code Interpreter

The diagram below illustrates how Amazon Bedrock Agent integrates with Code Interpreter to handle data analysis, visualization, and complex computation tasks.

Main Workflow:

1. Customer submits a query or uploads a data file.
2. Analytics Agent in Amazon Bedrock receives the query and generates a response based on the Large Language Model (LLM). If code execution is required, the agent forwards the task to Code Interpreter.
3. Code Interpreter receives the input file or processing request, performs calculations, analysis, or data visualization.
4. Upon completion, Code Interpreter generates output files and sends the results back to Analytics Agent.
5. Analytics Agent consolidates the information and responds to the customer.

File Processing Workflow:

• Customers can upload data files for processing.
• Analytics Agent shares these files with Code Interpreter.
• Code Interpreter executes code on the input files and generates output files.
• These output files are shared with the customer for download or further use.

Key Features and Applications of Amazon Bedrock Agent

1. Multi-agent Collaboration
This feature allows developers to easily create, deploy, and manage specialized Agents, enabling them to work together to handle complex business processes.

2. Retrieval-Augmented Generation (RAG)
The Agent can connect to enterprise data sources to provide accurate responses to customer queries.

🔹 Example: A customer asks: “How did the history of Python begin?”

• Step 1: The AI searches for information from an external source (e.g., Wikipedia).
• Step 2: The AI synthesizes and generates a response based on the retrieved data.
• Result: “Python was created by Guido van Rossum in 1991 to make programming more readable and efficient.”

👉 Benefit: Enables the AI to answer questions for which data is not available in the model but can be retrieved from external sources.

3. Orchestrate and Execute Multi-step Tasks
Customers can select an AI model and provide simple instructions, such as:
“You are a warehouse management Agent, help identify available products in the system.”

The Agent will analyze the task, break it down into logical steps, and automatically call the necessary APIs to complete the request.

4. Memory Retention Across Interactions
Amazon Bedrock Agent can retain information from previous interactions, providing a seamless and personalized experience.

For example, if a customer previously inquired about inventory for product X, the Agent can remember this information and avoid requesting it again in subsequent queries.

5. Code Interpretation
The Agent can generate and execute code in a secure environment, automating complex analytical queries.

👉 Applications:
✅ Data analysis
✅ Data visualization (charts, graphs)
✅ Solving complex mathematical problems

6. Prompt Engineering
The Agent can automatically generate prompt templates from customer instructions, action groups, and knowledge bases.
Customers can also refine inputs, orchestration plans, and model responses to improve results.

Cost Considerations for Amazon Bedrock Agent

In this workshop, we use the Claude 3 Sonnet model, with specific costs as follows: