Empower Your Software Development with Amazon Bedrock Agents
Are you looking for a fresh perspective in building dynamic and complex business workflows? Look no further! With Amazon Bedrock Agents, you can now set up an agent to act as a software application builder assistant. Let’s explore how this innovative tool can revolutionize your workflow.
Agentic workflows are the future of building workflows, powered by large language models (LLM) as their reasoning engine. These workflows break down natural language queries into actionable steps with iterative feedback loops, enabling you to build advanced solutions efficiently. Amazon Bedrock Agents leverage the reasoning capability of foundation models to orchestrate multistep tasks, providing flexibility, dynamic workflows, and cost savings for your projects. By utilizing this tool, you can customize and tailor applications to meet specific requirements while ensuring data security.
Amazon Bedrock Agents work seamlessly with AWS managed infrastructure capabilities, reducing management overhead and automating repetitive tasks. With the power of AI automation, you can boost productivity and decrease costs, making your development process more efficient.
Amazon Bedrock offers a fully managed service with high-performing FMs from leading AI companies like AI21 Labs and Anthropic. This service provides a broad set of capabilities to build generative AI applications while upholding security, privacy, and responsible AI practices.
Solution Overview
Our generative AI-based application builder assistant simplifies tasks across all tiers of your software application. From generating code snippets for UI and backend tiers to recommending design best practices using AWS Well-Architected Framework, this assistant can streamline your development process and enhance productivity.
Not only can the agent generate SQL queries using natural language questions, but it can also execute them against a database instance, providing valuable insights for the database tier.
By leveraging two knowledge bases and the Retrieval Augmented Generation (RAG) technique, our assistant ensures accurate and comprehensive responses to user queries, enhancing the overall user experience.
Based on the workflows illustrated in the diagram, the assistant can address various use cases, including writing SQL queries, accessing design best practices, and generating and explaining code snippets.
Prerequisites
Before getting started with Amazon Bedrock Agents, ensure you have completed the following prerequisites:
- Clone the GitHub repository and follow the setup instructions outlined in the README file.
- Set up an Amazon SageMaker notebook on an ml.t3.medium Amazon EC2 instance using the provided CloudFormation template.
- Acquire access to models hosted on Amazon Bedrock to leverage the full capabilities of the platform.
Implement the Solution
Our GitHub repository notebook covers various learning objectives, including choosing the underlying FM, creating action groups, ingesting data into knowledge bases, and generating UI and backend code using LLMs.
Agent Instructions and User Prompts
The application builder assistant agent comes equipped with detailed instructions for handling different types of user queries, ranging from SQL generation to code explanation. Each user question includes a system prompt for a personalized and effective response.
Cost Considerations
While utilizing Amazon Bedrock Agents, keep in mind the cost considerations associated with embedding models, text model invocations, Amazon S3 usage, and Amazon OpenSearch Service pricing.
Clean Up
To avoid unnecessary costs, our solution includes an automated resource clean-up process after each run. Follow the instructions in the notebook to prevent automatic clean-up and explore different prompts.
Conclusion
By leveraging Amazon Bedrock Agents and multiple knowledge bases, you can create a powerful generative AI-based software application builder assistant that enhances your software development process. From generating code snippets to recommending design best practices, this assistant is your go-to solution for efficient and productive development.
Acknowledgements
We would like to thank all the reviewers for their valuable feedback.
About the Author
Shayan Ray is an Applied Scientist at Amazon Web Services, specializing in natural language processing and conversational AI. His research focuses on task-oriented dialogue systems and LLM-based agents, contributing to advancements in AI technology.