Agentic workflows are revolutionizing the way businesses approach dynamic and complex use cases by leveraging large language models (LLMs) as their reasoning engine. These workflows break down natural language tasks into actionable steps, incorporating iterative feedback loops to deliver final results efficiently. The importance of measuring and evaluating the robustness of these workflows, especially when dealing with adversarial or harmful content, cannot be overstated.
Enter Amazon Bedrock Agents – the solution that transforms natural language conversations into task sequences using groundbreaking prompting techniques such as ReAct and chain-of-thought (CoT) with LLMs. This flexibility enables dynamic workflows, reduces development costs, and offers customization options to tailor apps to specific project requirements while ensuring data privacy and application security. Leveraging AWS managed infrastructure capabilities and Amazon Bedrock further streamlines operational overhead.
While Amazon Bedrock Agents come equipped with mechanisms to prevent general harmful content, users can enhance protection with custom-defined guardrails using Amazon Bedrock Guardrails. These additional safeguards, built on top of the existing foundation models (FMs), offer industry-leading safety measures by filtering out harmful content and preventing misleading responses in scenarios like Retrieval Augmented Generation (RAG) and summarization workloads, enabling users to enforce safety, privacy, and truthfulness in a single solution.
In this article, we delve into how Amazon Bedrock Agents, when combined with Amazon Bedrock Guardrails, can strengthen robustness for domain-specific use cases.
Solution Overview
Let’s consider a sample use case for an online retail chatbot that necessitates dynamic workflows for tasks like searching and buying shoes based on customer preferences through natural language queries. We build an agentic workflow using Amazon Bedrock Agents to address this scenario.
To assess its adversarial robustness, we challenge the chatbot by prompting it to provide fiduciary advice on retirement planning. This exercise highlights the importance of robustness and showcases how Amazon Bedrock Guardrails can enhance protection and prevent the chatbot from offering inappropriate advice.
For this chatbot, the preprocessing stage of the agent, before invoking the LLM, remains disabled by default. However, in certain cases, fine-grained control is essential to distinguish what constitutes acceptable inputs. For instance, a retail agent giving retirement advice falls outside the scope of the product and might result in negative consequences like customer trust issues. Such scenarios underscore the need for tailored robustness controls.
Another critical aspect is safeguarding personally identifiable information (PII) generated by agentic workflows. By configuring Amazon Bedrock Guardrails within Amazon Bedrock Agents, organizations can bolster robustness against regulatory compliance requirements and unique business needs without the need for intricate LLM fine-tuning.
The subsequent diagram illustrates the solution architecture:
Incorporating AWS services like Amazon Bedrock, Amazon Bedrock Agents, Amazon Bedrock Guardrails, IAM, Lambda, and SageMaker, we further solidify the framework for enhanced operational efficiency.
To continue exploring these concepts and run practical examples using Jupyter notebooks, refer to the GitHub repository provided.
Prerequisites
Before diving into the demo, ensure you have:
- An AWS account
- Cloned the GitHub repository and followed the setup instructions outlined in the README
- Set up a SageMaker notebook using the included CloudFormation template
- Access to required models on Amazon Bedrock
Conclusion
By leveraging Amazon Bedrock Guardrails to enhance the robustness of agentic workflows, businesses can effectively mitigate risks and ensure compliance with privacy and safety regulations. The integration of guardrails provides an added layer of protection, preventing agents from deviating into sensitive areas and maintaining integrity within the defined use case boundaries.
Through proactive measures like Amazon Bedrock Guardrails, businesses can confidently deploy AI solutions that not only deliver on functionality but also adhere to stringent safety standards and privacy considerations, establishing trust with users and stakeholders alike.
Acknowledgements
We extend our gratitude to all reviewers for their invaluable feedback and contributions.
About the Author
Shayan Ray is an Applied Scientist at Amazon Web Services, specializing in natural language processing and AI technologies. With a focus on conversational AI and LLM-based solutions, Shayan’s research explores the intersection of language understanding, personalization, and reinforcement learning.