Remember when computers just calculated things and occasionally froze during your most important presentation? Those were simpler times. Now, Generative AI applications are everywhere, doing everything from text creation to task automation and user interactions that actually feel… human-ish?

The possibilities are endless—intelligent chatbots that understand your existential 3AM questions, automation tools sophisticated enough to make sci-fi writers nervous—but let’s be honest: building a functional Generative AI application is about as straightforward as managing a complex project with constantly evolving requirements. Many businesses face significant challenges with reliability, efficiency, and ensuring their AI systems effectively understand and respond to user needs.

If developing AI applications has often felt like deciphering complex instructions in an unfamiliar language, Amazon Bedrock Agents offer both the right tools and a clear translation.

So, if you are looking to simplify your AI development journey without sacrificing that spark of digital brilliance, Amazon Bedrock Agents provides the sturdy foundation you need—less quicksand and more launchpad. They’re transforming AI development from “maybe someday” to “check out what we launched last Tuesday!”

In this blog, we’ll dive into nine key strategies to help you build high-performing, cost-efficient, and scalable Generative AI applications using Amazon Bedrock Agents. Let’s get started!

Best Practices for Building Generative AI Applications with Amazon Bedrock Agents

1. Define Clear Use Cases and Objectives

Building a successful Generative AI application using Bedrock Agent hinges on establishing a solid foundation of high-quality ground truth data and meticulously defined instructions. This foundational work is crucial for creating agents that are reliable, predictable, and effective. Let’s explore a structured approach to building this essential base.

Collect Ground Truth Data as the Foundation for Agent Training

Before getting into agent development for your Generative AI applications, collecting accurate, real-world data, interactions, and conversations is crucial. This “ground truth” serves as the agent’s training data, providing concrete examples of desired behavior in various situations. It’s the bedrock upon which the agent learns and refines its responses.

Consider this expanded example of ground truth data for a banking assistant agent, incorporating more realistic scenarios and data points:

Figure 1: Ground Truth Data for a Banking Assistant Agent

This example demonstrates the importance of capturing a wide range of user queries, including variations in phrasing and intent. The inclusion of session attributes allows the agent to maintain context and personalize responses. Critically, it shows the specific APIs and systems that should be invoked for each scenario, providing clear instructions for the agent’s actions.

Establish a Clear Scope and Interaction Framework for Agents

Setting clear boundaries for the agent’s functionality is essential for controlling its behavior and ensuring it stays within its designated area of expertise. A structured framework is crucial, including:

1. Primary Functions: Clearly define the agent’s core capabilities. For example, a customer service agent might handle order tracking, returns, and basic product inquiries.
2. Out-of-Scope Activities: Equally important is defining what the agent should not do. This prevents the agent from attempting tasks it’s not equipped for or accessing sensitive information it shouldn’t handle. For our customer service example, out-of-scope activities could include processing payments, handling technical support issues requiring deep expertise, or providing financial advice.
3. Expected Inputs and Outputs: Specify the types of inputs the agent can accept (e.g., natural language, structured data) and the formats of its outputs (e.g., text, JSON). Define clear error-handling procedures for when the agent encounters unexpected input or cannot complete a task. This includes how the agent should communicate errors to the user in a helpful and informative way.

Craft Unambiguous Instructions

Clear, unambiguous instructions are critical for consistent agent performance. Avoid vague language and focus on specific, actionable steps. Here’s a comparison of effective and ineffective instructions:

Ambiguous Instruction: “Process the user’s request.”

Clear Instruction:

1. Identify the user’s intent using the identify_intent() function.
2. Based on the identified intent:
   • If the intent is “get_balance,” call the get_account_balance() API with the user’s ID. 
   • If the intent is “transfer_funds,” collect the necessary transfer details from the user and then call the transfer_funds() API. 
   • If the intent is “open_account,” display the account opening form to the user. 
3. Handle any errors returned by the APIs and inform the user accordingly.

This level of detail ensures the agent knows exactly what to do in each scenario.

Refine Agent Personality and Tone for a Cohesive User Experience

The agent’s personality and tone should align with its purpose and your brand. Provide explicit instructions on how the agent should communicate. For example:

• Formal Customer Service Agent: “Maintain a professional and courteous tone. Address users by their preferred title (Mr., Ms., etc.). Avoid slang and use formal language.”
• Informal Customer Support Agent: “Use a friendly and approachable tone. Address users by their first name. Use clear and concise language, avoiding technical jargon where possible. Use emojis sparingly to add personality.”

Clear instructions and well-defined scope serve as the foundation for all the other best practices. They guide your agent’s behavior, ensure consistent performance, and make it easier to maintain and update your agent as requirements evolve.

2. Optimize Model Selection for Cost and Performance

Model selection significantly impacts both the performance and cost of your agent implementation. Different tasks require different levels of sophistication from the underlying foundation model.

For simple, straightforward tasks, consider using more efficient models like Claude 3 Haiku. For complex reasoning tasks that require sophisticated understanding, Claude 3 Opus might be more appropriate. Implement A/B testing to compare model performance and cost across different scenarios.

Model selection isn’t a one-time decision. Regular evaluation of model performance against your specific use cases helps optimize both effectiveness and cost efficiency.

3. Build Small and Focused Agents that Interact with Each Other

Instead of creating monolithic, all-encompassing agents, a microservices-inspired approach to agent design offers significant advantages. Building small, focused agents that interact with each other leads to improved maintainability, scalability, and flexibility in your AI applications. This modular design allows for specialized optimization and independent evolution of each agent.

Divide and Conquer Complex Tasks

Consider a corporate support system. Instead of a single, massive agent attempting to handle HR, IT, and facilities management, a more effective strategy is to create specialized agents for each domain. An HR agent can focus on employee benefits and policies, while an IT agent handles technical support requests. A facilities management agent can address issues related to building maintenance and office resources.

This decomposition into specialized agents mirrors the microservices architecture in software development. Each agent becomes a self-contained unit of functionality, responsible for a specific set of tasks.

Facilitate Inter-Agent Communication

Your agents may be specialized, but they don’t work in isolation. Ensuring they can communicate when needed creates a more integrated and efficient system. For example, if an employee requests a new laptop (IT task) through the HR agent, the HR agent can seamlessly hand off the relevant information to the IT agent, streamlining the request process.

Figure 2: Scalable Multi-Agent Architecture

This inter-agent communication can be implemented using various methods, such as message queues, APIs, or shared data stores. The choice of method will depend on the specific requirements of the application and the complexity of the interactions.

The multi-agent architecture offers several advantages: Each agent can be optimized for its specific task, testing becomes more straightforward, and updates can be made without affecting the entire system. This approach also allows us to use different foundation models for different tasks, optimizing both performance and cost.

Getting AI agents to collaborate effectively can be tricky. Our recent blog breaks down how Multi-Agent Bollaboration on Amazon Bedrock really works!

4. Integrate Actions and APIs Thoughtfully

Your agent’s effectiveness largely depends on how well it can interact with your existing systems. When integrating actions and APIs, focus on creating clear, purpose-driven interfaces that the agent can easily understand and utilize.

For example, in a CRM system integration, instead of exposing the entire customer database API, create specific actions for common tasks like “retrieve customer history” or “update contact information.” Each action should have clear input parameters and expected outputs, making it easier for the agent to understand when and how to use them.

The key is to design your APIs with the agent’s reasoning capabilities in mind. Include descriptive names and documentation that help the agent understand the purpose and appropriate use cases for each action.

5. Optimize Your Knowledge Base Design

A well-structured and efficiently retrievable knowledge base is the lifeblood of an intelligent agent. It’s the source of truth that empowers your agents to provide accurate, contextually relevant responses. Therefore, careful design, focusing on both content organization and retrieval efficiency, is paramount.

Implement Logical Segmentation to Structure Knowledge for Intelligent Access

Begin by organizing your content into logical segments that directly align with your agent’s tasks and responsibilities. This ensures that the agent can quickly locate the information it needs. For example, a technical support agent’s knowledge base could be divided into sections like product documentation, troubleshooting guides, FAQs, and common solutions. A sales agent’s knowledge base might include product catalogs, pricing information, competitor analysis, and sales scripts. This segmentation allows the agent to focus its search and retrieve relevant information more efficiently.

Within these segments, implement effective chunking strategies. Break down large documents or articles into smaller, more manageable chunks of information. This allows the agent to retrieve only the most relevant information for a given query, rather than having to process entire documents. Chunking can be done based on paragraphs, sections, or even individual sentences, depending on the nature of the content and the complexity of the queries.

Manage Dynamic and Static Information

Recognize the temporal nature of your data. Some information, such as pricing or product availability, might require frequent updates, while other content, like historical data or company policies, remains relatively static. Design your knowledge base and update mechanisms accordingly. Implement automated processes for updating dynamic information to ensure accuracy and timeliness.

Version control is crucial for maintaining consistency and tracking changes to your knowledge base. It allows you to revert to previous versions if necessary and provides a clear audit trail of all updates. This is particularly important for regulated industries or when dealing with sensitive information. Consider using a version control system like Git to manage your knowledge base content.

6. Test and Iterate in Real World Scenarios

Testing your agent shouldn’t be limited to controlled environments. Real-world testing helps identify edge cases and unexpected user behaviors that might not be apparent in development.

Figure 3: Testing Frameworks (source: aws.amazon.com)

Begin with a comprehensive set of test cases derived from actual user interactions. For a customer service agent, this might include historical customer queries, common request patterns, and known edge cases. Test, not just for correct responses, but also for appropriate handling of ambiguous or incomplete requests.

Implement a systematic testing approach that covers:

• Response accuracy and relevance
• Task completion rates
• Handling of edge cases
• Performance under various loads
• Integration with other systems

7. Leverage Infrastructure as Code

Treat your agent development like any other software project by using Infrastructure as Code (IaC) frameworks. This approach enables iterative, consistent, and automated deployments, allowing you to build production-ready agents that can be easily reproduced, tested, and monitored. This practice extends beyond just the agent definitions to include all associated resources.

Figure 4: Reusability Of A Utilities Action Group (source: aws.amazon.com)

Implement infrastructure as code using AWS CloudFormation, AWS CDK, or Terraform to define your entire agent ecosystem, including:

• Agent configurations and versions
• API integrations and Lambda functions
• Knowledge base connections
• Security settings and IAM roles
• Monitoring and logging configurations

This approach ensures reproducibility and makes it easier to manage multiple environments (development, staging, production) while maintaining consistent configurations.

8. Implement Robust Security and Access Control

Security should be a fundamental consideration in your agent implementation, not an afterthought. Implement comprehensive security measures that protect both your data and your users.

Start with proper IAM role configuration, ensuring that your agent has only the permissions it needs to perform its tasks. Implement encryption for data at rest and in transit and ensure that sensitive information is properly handled throughout the agent’s workflow.

Consider implementing:

• Fine-grained access controls for different user groups
• Audit logging for all agent actions
• Data encryption and masking mechanisms
• Regular security reviews and updates

9. Monitor and Debug Agent Execution

Effective monitoring and debugging capabilities are crucial for maintaining and improving your agent’s performance over time. Implement comprehensive monitoring that provides visibility into your agent’s operation.

You can utilize Amazon CloudWatch for monitoring key metrics such as:

• Response times and latency
• Error rates and types
• Usage patterns and peak loads
• Cost metrics and resource utilization

By implementing detailed logging, you can capture the agent’s decision-making process, including the steps it takes to complete tasks and any errors or unexpected conditions it encounters.

Develop Smarter Generative AI Applications Using Bedrock Agents with Cloudelligent

Successfully implementing intelligent agents with your Generative AI applications requires a strategic approach—one that balances accuracy, cost-efficiency, security, and scalability. By following these best practices, you can ensure that your Amazon Bedrock Agents are optimized for real-world scenarios, delivering intelligent and efficient responses tailored to your business needs.

At Cloudelligent, we specialize in helping businesses harness the full potential of AWS AI/ML services. Whether you’re looking to design, deploy, or optimize your Generative AI applications, our team of experts is here to guide you every step of the way.

Take the first step toward AI innovation today! Get a Free AI & ML Assessment and explore how Cloudelligent can help accelerate your AI journey.