Optimizing Agent Performance: The Role of Versioned Datasets in Agent Evaluation

Introduction to Agent Evaluation

The Importance of Stable Inputs and Ground Truth

Workflow: An Example with a Financial Market-Intelligence Agent

Why Datasets Matter in Agent Evaluation

The Inner and Outer Loops of Evaluation Process

Types of Test Scenarios for Robust Evaluation

Exploring the Market Trends Assistant Agent

Implementation Steps for Effective Evaluation

Using Datasets to Enhance Your Workflow

Best Practices for Sustainable Agent Evaluation

Conclusion: Building a Reliable Test Suite

About the Authors

Enhancing Agent Evaluation with Versioned Test Datasets in Amazon Bedrock AgentCore

When it comes to evaluating AI agents, particularly in dynamic environments like financial markets, having reliable benchmarks is crucial. The key to effective evaluation lies in combining fast-moving online signals with fixed offline baselines. This combination helps determine whether your AI agent is genuinely improving over time.

The Importance of Stable Evaluation

To truly assess improvements, you need a stable dataset alongside your changing real-world traffic. This is where managing test cases as datasets in Amazon Bedrock AgentCore shows its immense value. It enforces version control on test cases, allowing you to author scenarios with inputs, expected outputs, assertions, and tool sequences. By publishing these as immutable versions, they maintain consistency across evaluation runs, while mutable drafts allow for continuous improvement before locking in changes.

In this post, we’ll explore a full workflow using a financial market-intelligence agent, capturing failures from production, building a versioned dataset, running evaluations, fixing the agent, and confirming improvements with a robust framework.

Why Datasets Matter

AI agents are inherently non-deterministic. The same input can yield varying outputs across different runs, rendering single evaluation results nearly meaningless. Are your scores improving because the agent has changed, or simply due to random sampling variations? Consistent measurement grounded in stable inputs is essential for reliable evaluations.

Stable inputs alone, however, aren’t sufficient. Whereas a large language model (LLM) can evaluate whether a response “sounds” helpful, it cannot verify the accuracy of stock prices, the correct sequence of operations, or detect leaks of personally identifiable information (PII). You need “ground truth”—the expected response, required tool sequences, and assertions that remain fixed regardless of variations in phrasing.

Understanding the Evaluation Loops

Inner Loop: The Developer Desk

During the development phase, agents are invoked for quick evaluations, but often with arbitrary test cases. When scores improve, developers want to attribute that to fixes made. However, without stable inputs, it’s impossible to determine if the agent truly improved or if the inputs simply became easier.

Outer Loop: CI/CD Pipeline

Before deploying changes, teams should ensure nothing broke. However, many lack a stable, versioned set of inputs with explicit assertions, leading to unreliable results. Passing a CI/CD gate based on fluctuating questions fails to catch regressions and undermines trust in the evaluation process.

Bridging the Gap with Versioned Datasets

A versioned dataset improves the evaluation workflow by closing the gap between the inner and outer loops. Developers curate failures in a mutable draft, while published versions serve as the outer loop gate. These versions are immutable, ensuring consistency in what is tested across different iterations and preventing regression from vague criteria.

Types of Test Scenarios

Amazon Bedrock AgentCore supports two schema types tailored for these evaluation loops:

1. Predefined Scenarios: These scenarios are retrospective and include exact user queries and expected outputs. Failures formalized through predefined scenarios persist in every future evaluation. They provide explicit, repeatable criteria for evaluation.

   Example:

   PreDefinedScenario{
      "scenario_id": "broker_profile_onboarding",
      "turns": [...] // Specific user input
   }

2. User Simulation Scenarios: These are prospective scenarios where personas drive conversations. Instead of detailed scripts, these scenarios emerge dynamically, allowing for broader coverage and identifying failure modes that might not be anticipated.

   Example:

   SimulatedScenario(
      scenario_id="sim-tech-analyst-nvda-amd-deep-dive",
      actor_profile=ActorProfile(traits={...}),
      input="I'm prepping for a client call and need...",
      // Max turns and assertions
   )

User simulation is particularly impactful in the inner loop, surfacing failures that can be captured for future predefined scenarios.

Implementation Walkthrough

Here’s a concise hands-on guide for setting up these evaluations:

Prerequisites

• AWS account with permissions for AgentCore.
• AWS CLI configured.
• Clone the AgentCore Samples Repository.

Walkthrough Steps

1. Deploy the Market Trends Agent:
   Run the deployment script to provision necessary resources.

2. Create and Version Evaluation Datasets:
   Use a management script to create datasets, curating relevant test cases like broker onboarding and pricing checks.

3. Run Evaluation Against Versioned Dataset:
   Load simulated scenarios, invoke the agent, and evaluate performance using metrics like Correctness and Helpfulness.

4. Iterate: Fix and Re-Evaluate:
   Implement changes based on evaluation results, draft new examples, and re-run evaluations to ensure improvements are validated against the same scenarios.

5. View Results:
   Insights and scores are available in the AgentCore console and CloudWatch logs.

Benefits of Using Versioned Datasets

Managing datasets efficiently allows you to build a repository of institutional knowledge from past failures. By grounding your evaluation processes in real-world incidents, you ensure that your scenarios are genuinely reflective of the challenges your agents face.

1. Predefined for Depth, Simulated for Breadth: This balanced approach ensures your evaluation captures both known issues and unexplored challenges.

2. Publish Before Changes: Immutable versions allow for easy tracking and debugging in the future.

3. One Dataset, Many Versions: This promotes continuity and leverages past knowledge without starting from scratch.

4. Cleanup Practices: To avoid future charges, ensure datasets and agents are deleted once evaluations are complete.

Conclusion

A static test suite is vital for accurate agent evaluation. Managing datasets in AgentCore provides an organized, versioned, and schema-validated framework for agent assessment. By transforming production failures into permanent regression scenarios and utilizing simulation to broaden coverage, you galvanize the evaluation process, ensuring each agent iteration is accountable.

Get started with AgentCore’s documentation and the detailed sample implementations. For any team committed to reliable AI deployment, making these practices a laid foundation will maximize both performance and trust.

About the Authors

Visakh Madathil is a Solutions Architect at AWS, focused on enhancing the reliability of AI in production. Bharathi Srinivasan is a Generative AI Data Scientist at AWS, dedicated to promoting responsible AI. Together, they aim to foster trust and reliability in machine learning applications.