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testing-strategy

Design test strategies for AI agents covering unit, integration, and memory tests.

21|5|Updated Mar 30, 2026
Search Tags:#mocking#unit-testing#integration-testing#ai-agent#test-strategy#memory-testing
One-click install
npx skills add https://github.com/aws-samples/sample-agent-greenhouse --skill testing-strategy-aws-samples
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: testing-strategy
Source: https://github.com/aws-samples/sample-agent-greenhouse/tree/main/src/platform_agent/plato/skills/testing-strategy
Command: npx skills add https://github.com/aws-samples/sample-agent-greenhouse --skill testing-strategy-aws-samples

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

AI agents often falter without robust testing across unit, integration, memory, and tool interactions. This skill provides a structured approach to design comprehensive test strategies that catch regressions early and ensure predictable behavior in production.

Core Features & Use Cases

  • Unit Testing: validate individual tools, hooks, and memory operations with mocks.
  • Integrations: verify end-to-end agent workflows with mocked models and services.
  • Agent Evaluation: define golden tests and metrics for quality gates.
  • Memory & Tool Mocking: ensure isolation of memory backends and tool invocations.
  • Guardrails: embed safety checks and failure handling into test plans.

Quick Start

Instruct the assistant to craft a complete test strategy plan for a new AI agent, including unit, integration, and memory tests, with concrete examples and success criteria.

Frequently Asked Questions about testing-strategy

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I design a test strategy for an AI agent?▼

AI agent memory testing validates individual memory operations and backend isolation using mocks. This ensures that conversation flows and tool interactions maintain state correctly without unintended side effects.

How do I mock dependencies for AI agent integration tests?▼

Mocking dependencies for integration tests involves isolating memory backends and mocking external models or services. This verifies end-to-end agent workflows while maintaining deterministic test results.

What are guardrails in an AI agent test plan?▼

Guardrails in an AI agent test plan embed safety checks and failure handling directly into the testing workflow. They provide actionable criteria for quality gates to ensure production readiness.

How do I create deterministic test results for AI agents?▼

Deterministic test results for AI agents are achieved by defining golden tests, using mocked models, and establishing concrete evaluation metrics. This isolates tool invocations and memory backends for predictable outcomes.

Do I need unit tests and integration tests for AI agent tools?▼

Yes, unit tests validate individual tools, hooks, and memory operations with mocks, while integration tests verify end-to-end agent workflows. Both are required to catch regressions and ensure predictable behavior.

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