Evaluate skill-equipped agents with Strands Evals and Amazon Bedrock AgentCore
Skills let you encode domain-specific procedures as reusable, portable instructions for agents, but a fluent answer doesn't prove the agent picked the right skill or followed it. Learn how to measure skill selection and instruction following with Strands Evals and Amazon Bedrock AgentCore Evaluations.
General-purpose agents handle a broad range of tasks, but they still need to follow specific procedures like compliance checks or document processing workflows. Encoding all these rules in a single system prompt or application logic becomes difficult to maintain and update. Skills offer a modular alternative, allowing agents to learn domain-specific tasks through reusable sets of instructions stored in SKILL.md files.
Skills package tools, context, knowledge, workflow, and guardrails, making it easier for teams to specialize agents and update domain-specific guidance without fine-tuning the underlying model or rewriting agent logic.
However, skills introduce two failure modes that general output-quality metrics can miss. An agent may invoke an inappropriate skill for a task or invoke the correct skill but skip or only partially follow its instructions. Skill-focused evaluators in Strands Evals SDK and Amazon Bedrock AgentCore Evaluations address these issues by measuring skill selection accuracy, skill instruction following, and the deterministic loading of named skills.
Skill Selection Accuracy determines whether each invoked skill is appropriate for the task, returning a binary result for each skill. Skill Instruction Following assesses how fully the agent follows an invoked skill's instructions, providing a five-level rating grounded in evidence for each prescribed step. Skill Invoked is a binary, deterministic check in Strands Evals, indicating whether a named skill has been loaded successfully.
These evaluators can be used with a recorded trajectory in Strands Evals or an OpenTelemetry trace in an observability layer. They help identify and address issues like inappropriate skill selection or incomplete instruction following, which can produce plausible responses without using pre-determined domain knowledge. By understanding each evaluator's purpose and applying them to agent evaluations, teams can ensure agents follow the correct procedures and maintain consistency in domain-specific knowledge.
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