Skill Detail

Benchmark virtual agents with scripted multi-turn conversations using Agent Evaluation

Run concurrent scripted conversations against a target agent to measure whether it stays on task, responds correctly, and holds up in repeatable test cases.

Runbooks & DiagnosticsCustom Agents
Runbooks & Diagnostics Custom Agents Published
⭐ 358 GitHub stars
INSTALL WITH ANY AGENT
npx skills add agentskillexchange/skills --skill benchmark-virtual-agents-with-scripted-multi-turn-conversations-using-agent-evaluation Copy
Works best when you want a reusable capability, not another fragile one-off prompt.
At a glance
Tools required
Python environment, target agent endpoint or integration, optional AWS services such as Bedrock or SageMaker
Install & setup
Clone the repository and follow the upstream documentation to configure an evaluator agent, define scripted test cases, and run benchmarks against your target agent.
Author
AWS Labs
Publisher
Open Source Project
Last updated
Apr 19, 2026
Quick brief

Use Agent Evaluation when you want an evaluator agent to run scripted, multi-turn test conversations against a target agent and score the responses. The upstream project is explicit about this workflow: define cases, orchestrate conversations, evaluate results during the run, and plug the checks into CI or broader testing.

How it works

What this skill actually does

Invoke this instead of manual spot-checking or a generic hosted agent platform when the need is repeatable conversation-based benchmarking. The scope boundary is clear: Agent Evaluation tests target agents through scripted interactions. It is not a general AWS product listing or broad agent framework card.