**Global tech industry**: AWS introduces the Open Source Bedrock Agent Evaluation framework to robustly assess and optimise Amazon Bedrock Agents, enhancing AI-driven automation across industries with detailed metrics, multi-agent support, and integration with Langfuse for real-time performance insights.
Artificial intelligence (AI) agents are increasingly becoming essential in automating complex tasks, improving decision-making, and streamlining operations across diverse industries. The integration of AI agents within production environments necessitates robust, scalable evaluation frameworks to accurately assess their performance and effectiveness. Amazon Web Services (AWS) has responded to this need with the introduction of the Open Source Bedrock Agent Evaluation framework, which provides comprehensive tools to measure and enhance the performance of Amazon Bedrock Agents.
Amazon Bedrock Agents utilise foundation models accessible through Amazon Bedrock APIs to decipher user requests, retrieve pertinent information, and accomplish tasks efficiently. This capability enables businesses to automate multi-step processes by seamlessly interfacing with internal systems, APIs, and data sources, thereby allowing human teams to focus on more strategic activities.
The Open Source Bedrock Agent Evaluation framework addresses significant technical challenges faced by AI developers, particularly in evaluating end-to-end agent performance. While Amazon Bedrock already offers evaluation capabilities for foundation language models and retrieval-augmented generation (RAG), it lacks specific metrics tailored for the holistic assessment of Amazon Bedrock Agents, individual task steps, and multi-agent systems across various use cases. The framework facilitates detailed assessments by analysing agent goals, task accuracy, and chain-of-thought reasoning, across single and multiple turns in dialogue.
The framework integrates with Langfuse, an open-source LLM engineering platform, providing visual dashboards for tracing, debugging, and optimising AI agents. Langfuse captures detailed invocation traces, including input and output tokens, costs, and evaluation metrics, allowing developers to review agent performance comprehensively.
Key evaluations within the framework include:
-
Retrieval Augmented Generation (RAG): Measured by faithfulness (consistency with retrieved context), answer relevancy, context recall, and semantic similarity between generated and ground truth responses.
-
Text-to-SQL: Assessed for semantic equivalence of generated SQL queries to reference queries and the correctness of answer representation.
-
Chain-of-Thought Reasoning: Evaluated on helpfulness, faithfulness to given context, and adherence to explicit instructions.
Agents are tested using “trajectories,” which simulate user interactions consisting of sequential questions and expected answers, allowing performance to be evaluated realistically.
An illustrative application of the framework is its use with pharmaceutical research agents designed to accelerate cancer biomarker discovery. This multi-agent system includes specialised sub-agents for biomarker database analysis, statistical evaluation, clinical evidence research, and medical imaging, coordinated by a supervising agent. The framework evaluated the system across 56 questions organised into 21 trajectories, analysing individual sub-agent and supervisory responses. Metrics displayed through Langfuse revealed average scores indicating strong performance in chain-of-thought tasks (Helpfulness 0.77, Faithfulness 0.87, Instruction Following 0.69) and variable task accuracy results across different tool uses.
Security considerations include enabling model invocation logging within Amazon Bedrock to securely capture prompts and responses, and ensuring compliance with relevant regulatory certifications before deploying agents in production.
The Open Source Bedrock Agent Evaluation framework provides AI developers with a streamlined method to evaluate and refine Amazon Bedrock Agents, facilitating rapid iterations and optimisation through integrated metrics and trace visualisation. For those wishing to deploy the solution, detailed instructions and sample agent setups are available to guide users through implementation and evaluation processes.
The work was presented by Hasan Poonawala (Senior AI/ML Solutions Architect at AWS specialising in healthcare and life sciences), Blake Shin, and Rishiraj Chandra (both Associate Specialist Solutions Architects at AWS), who bring extensive experience in AI/ML technologies and cloud-based solution development.
Source: Noah Wire Services
