The Bottom Line: Amazon Bedrock AgentCore introduces versioned test datasets that enable stable evaluation of agents. With immutable versions for CI/CD gates and draft mode for development, it provides ground truth for verifiable measurements instead of subjective assessments—ideal for inner-loop iteration and regression control.
Amazon Bedrock AgentCore enables developers to create test suites with versioned datasets that grow with their agents. By combining online signals with stable offline baselines, improvements can be measured reliably—with immutable test versions serving as control points.
Agent evaluation achieves maximum relevance by combining fast online signals with stable offline baselines. To recognize genuine improvements, a fixed benchmark set is needed alongside changing live traffic.
Managing test cases as a versioned dataset in Amazon Bedrock AgentCore brings systematic discipline to agent evaluation. Developers can define scenarios with inputs, expected outputs, assertions, and tool sequences, then publish them as immutable, numbered versions. A flexible draft system allows free iteration until a control point is locked in. Production failures are automatically converted into permanent test cases for future changes.
Why Datasets Are Critical
Agents are intentionally non-deterministic. The same input can produce different outputs, making individual evaluation results almost meaningless. A shifted score can stem from either agent changes or different LLM sampling. Only consistent measurement across stable inputs reveals genuine improvements.
Stable inputs alone are insufficient, however. An LLM judge assesses whether an answer sounds helpful—not whether stock prices are correct, tool sequences executed in the right order, or whether personally identifiable data leaked between sessions. These checks require ground truth: the expected answer, the required tool sequence, and immutable assertions. Ground truth transforms subjective evaluation into verifiable measurement.
Versioned datasets provide both: stable inputs for comparable scores and ground truth for meaningful measurements. This is critical in two evaluation scenarios:
Inner Loop (Developer Desk): Developers call the agent, read scores, adjust tool descriptions, and iterate—on a minute-by-minute cadence. The problem: test cases are often ad-hoc, questions from a week ago, or randomly saved sessions. Without stable inputs, it cannot be determined whether the agent improved or the questions simply became easier.
Outer Loop (CI/CD Pipeline): Before deployment, regression control is required. Many teams have this gate, but often without stable, versioned inputs and explicit assertions. The pipeline tests against arbitrary inputs without ground truth—and misses regression errors that cause scoring changes.
Versioned datasets close this gap: During the inner loop, the developer curates failures into draft. In the outer loop, a published version becomes an immutable gate with complete ground truth—and thus becomes reliable regression control.
Source: aws.amazon.com