Build a test suite that grows with your agent with dataset management in Amazon Bedrock AgentCore

2026-05-28 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Intermediate, long

Summary

Amazon Bedrock AgentCore introduces dataset management to establish versioned test suites for evaluating AI agents, ensuring consistent performance measurement. This system allows users to author scenarios with inputs, expected outputs, assertions, and tool sequences, publishing them as immutable, numbered versions. It supports two primary scenario types: "Predefined scenarios" for explicit, backward-looking checks, often derived from production failures, and "User simulation scenarios" where an LLM-backed actor drives multi-turn conversations to uncover new failure modes. The article illustrates this with a "Market Trends Agent" example, detailing its five tools and common issues like stale prices, skipped identity checks, and PII bleed. The workflow involves deploying the agent, creating and versioning evaluation datasets, running evaluations, iterating on fixes, and re-evaluating against the same locked inputs to confirm improvements.

Key takeaway

For AI Engineers building and deploying conversational agents, consistently evaluating performance and preventing regressions is critical. You should integrate Amazon Bedrock AgentCore's versioned dataset management into your CI/CD pipelines and development loops. This allows you to establish immutable evaluation baselines, capture production failures as permanent test cases, and use simulated scenarios to proactively discover new failure modes, ensuring your agent improvements are genuinely effective and reliable.

Key insights

Versioned datasets with ground truth are crucial for reliable, comparable AI agent evaluation across development and CI/CD.

Principles

• Combine online signals with stable offline baselines for agent evaluation.
• Ground truth (expected response, tool sequence, assertions) turns subjective scores into verifiable measurements.
• Test suites should be grounded in real production incidents.

Method

Curate production failures into a mutable draft dataset, then publish immutable versions. Use on-demand runners for iteration and batch runners for large-scale baselines or CI/CD gates.

In practice

• Use predefined scenarios for known bugs, simulated for unknown failure modes.
• Publish a new dataset version before every agent change.
• Maintain one dataset ID across sprints for continuity.

Topics

• Amazon Bedrock AgentCore
• Agent Evaluation
• Dataset Management
• LLM Agents
• CI/CD Pipelines
• User Simulation

Code references

• awslabs/agentcore-samples
• aws-samples/amazon-bedrock-agentcore-samples

Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer

Related on AIssential

• Build reliable AI agents with Amazon Bedrock AgentCore Evaluations — Systematic, continuous evaluation is crucial for reliable AI agent performance in production.
• Introducing the agent performance loop: AgentCore Optimization now in preview — AgentCore Optimization introduces a data-driven loop for continuous AI agent improvement, replacing manual debugging.
• AWS Bedrock Agents: The Beginner’s Guide — AWS Bedrock Agents provide a managed platform for building and deploying AI agents with integrated orchestration and security.
• Semantic search across MCP tools with Amazon Bedrock AgentCore Gateway — Amazon Bedrock AgentCore Gateway enables scalable LLM agents through built-in semantic search for tool selection.
• Your Agent Passed Every Test. It's Still Going to Break in Production. — AgentOps requires a continuous evaluation loop to manage non-deterministic AI agent behavior in production.
• AI Dev 26 x SF | Ara Khan: Evals Are Broken Use Them Anyway — Evals are imperfect but essential for AI agent development, requiring nuanced interpretation and iterative improvement.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.