Building Robust and Scalable Intelligent Agents with Amazon Bedrock Agents
In Part 1 of this series, best practices for creating accurate and reliable agents using Amazon Bedrock Agents were explored. These agents help accelerate generative AI application development by orchestrating multistep tasks. They leverage foundation models (FMs) to create a plan and carry out the developer-provided instructions by using Retrieval Augmented Generation (RAG).
Now, in Part 2, we will dive into architectural considerations and development lifecycle practices that can help you build robust, scalable, and secure intelligent agents. Whether you are new to conversational AI or looking to enhance your existing agent deployments, this guide will provide valuable insights and practical tips to help you achieve your goals.
Enable Comprehensive Logging and Observability
Implementing thorough logging and observability practices from the outset is essential for debugging, auditing, and troubleshooting your agents. By enabling Amazon Bedrock model invocation logging and utilizing traces, you can track and troubleshoot your agents effectively.
Set up monitoring workflows to continuously analyze logs when moving agent applications to production. Consider using tools like Bedrock-ICYM for monitoring.
Use Infrastructure as Code
Utilize infrastructure as code frameworks to ensure repeatable and production-ready agents. AWS CloudFormation, AWS CDK, or Terraform can be used to write IaC code for Amazon Bedrock Agents. Start with the Agent Blueprints construct to deploy common agent capabilities easily.
Use SessionState for Additional Agent Context
Enhance your agent’s context by utilizing SessionState to pass information that is relevant to the agent’s operations. Use SessionAttribute and SessionPromptAttribute to provide additional context for the agent’s functionality.
Optimize Model Selection for Cost and Performance
Experiment with different foundational models to find the best balance of cost, latency, and accuracy for your agent’s application. Implement automated testing pipelines to make data-driven decisions on model selection.
Implement Robust Testing Frameworks
Automate agent evaluation to accelerate development and ensure high-quality solutions. Use frameworks like Agent Evaluation for comprehensive testing. Leverage Amazon Bedrock’s versioning and alias features for A/B testing.
Conclusion
Following these architectural and development best practices will empower you to create robust, scalable, and secure intelligent agents that seamlessly integrate with your systems. For further examples and resources, explore the Amazon Bedrock samples repository and engage with the Amazon Bedrock Workshop and Amazon Bedrock Agents Workshop.
About the Authors
Maira Ladeira Tanke is a Senior Generative AI Data Scientist at AWS with over 10 years of experience in machine learning.
Mark Roy is a Principal Machine Learning Architect for AWS, specializing in generative AI solutions.
Navneet Sabbineni is a Software Development Manager at AWS Bedrock, focusing on conversational AI services.
Monica Sunkara is a Senior Applied Scientist at AWS, with expertise in speech recognition and natural language processing.