Best practices for building robust generative AI applications with Amazon Bedrock Agents – Part 1

Building Intelligent AI Agents with Amazon Bedrock: Best Practices for Success

Creating AI agents that can understand and accurately respond to user inquiries is no easy task. Whether you’re working on a customer service chatbot or a virtual assistant, the journey involves meticulous planning and execution across several stages. From defining the capabilities of your agent to structuring a scalable infrastructure, there’s a lot to consider.

In this two-part series, we’ll delve into best practices for developing generative AI applications using Amazon Bedrock Agents. These agents streamline the development of generative AI applications by orchestrating multistep tasks. They leverage the reasoning capabilities of foundation models to break down user requests into manageable steps. With clear instructions from developers, agents create an orchestration plan, engaging with APIs and knowledge bases to provide accurate answers through Retrieval Augmented Generation (RAG) techniques.

Part 1 focuses on creating precise and reliable agents, while Part 2 will highlight architectural considerations and best practices throughout the development lifecycle.

Laying the Groundwork: Collecting Ground Truth Data

The bedrock of any successful agent is high-quality ground truth data—basically, the accurate real-world interactions that serve as benchmarks for evaluating models or systems. For a robust agent application, gathering a diverse set of these examples is crucial before you even begin building. This data acts as a reference for expected behavior, including the agent’s interactions with APIs and knowledge sources.

To assemble a solid ground truth dataset, aim for examples that span various user intents and scenarios. Include both simple and complex interactions, and regularly update your dataset as you learn about user behavior. It’s important to base your data on real customer interactions while ensuring privacy through de-identification and anonymization.

Example: A Banking Assistant Agent

Here’s a glimpse of what this might look like for a banking assistant that can check account balances and book appointments:

Defining Scope and Sample Interactions

Once you’ve gathered ground truth data, it’s time to define your agent’s scope—what it should and shouldn’t handle. Outline clear expected interactions between users and the agent. This step involves identifying key functions, capabilities, and limitations as well as determining expected input formats and output styles.

For example, in the case of an HR assistant agent, you might define its capabilities like this:

Primary Functions:

• Provide HR policies.
• Assist with vacation requests.
• Answer basic payroll queries.

Out of Scope:

• Handling sensitive employee data.
• Making hiring or firing decisions.
• Offering legal advice.

Expected Inputs:

• Natural language queries about HR policies.
• Requests for time-off information.

Desired Outputs:

• Concise responses to questions.
• Step-by-step guidance for vacation requests.

Defining these boundaries sets clear expectations and guides your development process, leading to a reliable AI agent.

Architecting Your Solution: Focused Agents That Work Together

When it comes to designing AI agents, remember the saying “divide and conquer.” Our experience shows that building small, focused agents that interact with one another is more effective than a single large monolithic agent. This strategy enhances modularity, maintainability, and scalability.

Take, for instance, both an HR assistant and a payroll assistant. Although they share some functionalities—like answering payroll inquiries—they work within different scopes and permissions. A collaborative multi-agent approach allows each agent to manage its own tasks without duplicating efforts.

Crafting the User Experience: Planning Agent Tone and Greetings

The personality of your agent is essential in shaping user interactions. Carefully strategizing the tone and greetings can create a consistent and engaging experience. Consider your brand voice, target audience preferences, and cultural sensitivities.

Here’s an example for two different types of agents:

Formal HR Assistant:
"You are an HR AI Assistant, helping employees understand company policies. Address users formally, using titles and last names."

Friendly IT Support Agent:
"You’re the IT Buddy, here to help with tech issues. Use a casual tone, address users by their first names, and sprinkle in some fun emojis and tech jokes."

Establish consistency across various agent interactions while ensuring that each tone aligns with your brand identity.

Maintaining Clarity: Providing Unambiguous Instructions

Clear communication is vital for effective AI agents. Ensure that instructions, definitions, and knowledge base integrations are straightforward and free of ambiguity. Using simple, direct language and providing specific examples will enhance clarity.

Ambiguous Prompt:
"Check if the user has time off available and book it if possible."

Clearer Prompt:

1. Verify the user’s time-off balance using the checkTimeOffBalance function.
2. Book the time off if available using bookTimeOff.
3. Inform the user if it’s not available and suggest alternatives.
4. Always confirm before finalizing bookings.

By providing explicit instructions, you reduce the likelihood of errors and ensure your agent behaves consistently.

Using Organizational Knowledge: Integrating Knowledge Bases

To empower your agents with enterprise knowledge, integrate them with existing knowledge bases. This integration enhances response accuracy and relevance while reducing the need for frequent updates to the model.

Here’s how to integrate a knowledge base with Amazon Bedrock:

1. Index Documents: Use Amazon Bedrock Knowledge Bases to index your documents.
2. Configure Access: Set up your agent to access the knowledge base during interactions.
3. Implement Citations: Reference source documents in the agent’s responses.

Make sure to keep your knowledge base updated, ensuring your agent has access to the latest information through event-based synchronization methods.

Defining Success: Establishing Evaluation Criteria

To evaluate your AI agent’s effectiveness, establish specific criteria. Consider these key metrics:

• Response Accuracy: Measure how well responses align with ground truth data.
• Task Completion Rate: Track the success percentage of task fulfillment.
• Latency: Analyze how quickly the agent responds.
• Conversation Efficiency: Assess how effectively the conversation collects information.
• Engagement: Evaluate the flow and relevance of responses.

Incorporate custom metrics that are specific to your use case, such as the number of raised HR tickets for an HR assistant.

Ongoing Evaluation: Implement a thorough evaluation process, testing with real user interactions and establishing a regular cadence for review and refinement.

Leveraging Human Evaluation

While automated metrics are beneficial, human evaluation remains crucial for an accurate performance assessment. Human feedback can provide insights that are hard to quantify automatically, including understanding natural language and identifying biases.

Best Practices for Human Evaluation:

• Gather a diverse panel of evaluators.
• Develop clear evaluation guidelines and rubrics.
• Collect both quantitative ratings and qualitative feedback.

Continuous Improvement: Testing, Iterating, and Refining

Building a successful AI agent is an iterative endeavor. Now that you have a prototype, extensive testing and continuous refinement are essential. This involves analyzing logs, conducting real-world testing, and regularly updating instructions and functionality.

Testing Strategy: Use AI to generate diverse test scenarios, ensuring your agent can handle both common and edge case inquiries effectively.

Analysis Tools: Enable agent tracing to gain insights into decision processes and make adjustments based on data analysis.

Conclusion

By following these best practices and committing to ongoing refinement, you can develop powerful and user-centric AI agents using Amazon Bedrock. Stay tuned for Part 2, where we’ll explore more on architectural considerations and effective scaling.

The AI Buzz Hub team is excited to see where these breakthroughs take us. Want to stay in the loop on all things AI? Subscribe to our newsletter or share this article with your fellow enthusiasts.