Optimal Strategies for Developing Resilient AI Applications with Amazon Bedrock Agents – Segment 1 | AWS Machine Learning Blog

Building Intelligent Agents

Building intelligent agents that can accurately understand and respond to user queries requires careful planning and execution across multiple stages. This series explores best practices for building generative AI applications using Amazon Bedrock Agents to accelerate application development.

High-Quality Ground Truth Data

The foundation of any successful agent lies in high-quality ground truth data. This data provides a benchmark for evaluating agent performance, testing, and identifying edge cases and pitfalls in user interactions.

Defining Agent Scope

It is crucial to clearly define the scope of each agent, including tasks it should handle, limitations, expected input formats, and desired output formats. By setting clear boundaries and expectations, you can guide development and create a reliable AI agent.

Agent Architecture

The principle of “divide and conquer” is valuable in agent architecture. Building small, focused agents that interact with each other promotes modularity, maintainability, and scalability. Multi-agent collaboration allows for reuse of functionalities and enhances scalability.

Agent Personality

The personality of your agent sets the tone for user interaction. Carefully plan the tone and greetings of your agent to create a consistent and engaging user experience aligned with your brand identity and audience preferences.

Effective Communication

Clear communication is essential for successful AI agents. Define instructions, functions, and knowledge base interactions using unambiguous language and specific examples to ensure predictability and reliability in agent behavior.

Integrating Knowledge Bases

Integrate your agents with organization knowledge bases to provide accurate, context-aware responses. Regularly update knowledge bases, control interaction with agents, and optimize semantic search capabilities to enhance response accuracy and relevance.

Evaluation and Testing

Define specific evaluation criteria to assess agent performance and track progress over time. Implement comprehensive testing using ground truth data, automated evaluation scripts, A/B testing, and human evaluation to refine and improve agent behavior iteratively.