What agent frameworks does this testing skill support?

It supports testing agents built with LangChain, LangGraph, CrewAI, AutoGen, OpenAI Assistants API, Anthropic tool-use, and custom frameworks. The test patterns are framework-agnostic—you define expected behaviors and the skill generates appropriate assertions regardless of the underlying agent architecture.

How do I integrate these tests into my CI/CD pipeline?

The skill generates pytest-compatible test suites with GitHub Actions workflow files included. Tests are categorized into fast (unit, mock-based), medium (integration with cached responses), and slow (live API calls). CI runs fast+medium on every PR and slow tests nightly, keeping your pipeline efficient while maintaining coverage.

How accurate is the hallucination detection?

The hallucination detection uses three layers: factual grounding checks against provided context, self-consistency tests across multiple runs, and structured output validation. For fact-based tasks, detection rates typically exceed 90%. For creative or open-ended tasks, it focuses on claim verification against source material rather than absolute truth detection.

How do I scale tests as my agent system grows?

The skill uses a modular test architecture—each agent capability gets its own test module with shared fixtures. As you add agents or tools, you add test modules without modifying existing ones. Response caching prevents redundant API calls, and parallel test execution keeps runtime manageable even with hundreds of test cases.

AI Agent Testing Framework

PRO

Advanced 20 min Verified 4.9/5

Create comprehensive test suites for AI agents with prompt regression tests, hallucination detection, reliability metrics, and CI/CD integration pipelines.

Last updated: February 9, 2026

Example Usage

Create a comprehensive test suite for my customer support AI agent:
Agent details:
Built with LangGraph (Python)
Tools: search_knowledge_base, create_ticket, escalate_to_human, check_order_status
Expected behaviors: Answer product questions accurately, create tickets for complaints, escalate billing issues, never fabricate order information
Known edge cases: Multi-language queries, angry customers, ambiguous requests
I need:
Prompt regression tests for 20 core scenarios
Hallucination detection for knowledge base responses
Tool selection accuracy tests
Latency benchmarks (must respond in <3 seconds)
Cost tracking per conversation
CI/CD integration with GitHub Actions
Weekly regression report generation

Skill Prompt

Pro Skill

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This skill works best when copied from findskill.ai — it includes variables and formatting that may not transfer correctly elsewhere.

How to Use This Skill

Copy the skill using the button above

Paste into your AI assistant (Claude, ChatGPT, etc.)

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Suggested Customization

Description	Default	Your Value
Type of agent to test: conversational, tool-using, multi-agent, RAG-based, or autonomous	`conversational`
Testing depth: smoke (10 tests), standard (30 tests), comprehensive (50+ tests with edge cases)	`comprehensive`
Output format: test-report (markdown), pytest-module (Python files), json-results, or dashboard-data	`test-report`
Test framework to generate for: pytest, unittest, jest, or vitest	`pytest`

Copy the skill above and paste it into Claude Code or your preferred AI assistant
Describe your AI agent: what it does, what tools it has, and what behaviors are critical
Specify your test framework preference (pytest, jest, etc.) and CI/CD platform
Review the generated test suite and customize thresholds to match your requirements
Run the tests locally and integrate into your CI/CD pipeline

What You’ll Get

Complete test suite architecture with unit, integration, evaluation, and performance tests
Prompt regression tests that catch behavior drift automatically
Hallucination detection with factual grounding and consistency checks
Tool selection accuracy tests for every agent capability
Performance benchmarks with latency, cost, and throughput metrics
Safety tests covering prompt injection, data leakage, and PII handling
GitHub Actions CI/CD workflow ready to deploy
Report generation for tracking quality trends over time

Tips for Best Results

Start with the 10 most critical agent behaviors and expand from there
Use response caching during development to keep test runs fast and free
Run comprehensive tests nightly; keep PR tests focused on regressions
Version your golden datasets alongside your prompts for traceability
Set realistic thresholds initially and tighten them as your agent improves

Research Sources

This skill was built using research from these authoritative sources:

Monte Carlo Data: AI Predictions 2026 — Testing as a First-Class Concern Industry analysis showing AI agent testing becoming a critical engineering discipline with dedicated tooling and metrics
Anthropic: Evaluating AI Outputs Official guidance on building evaluation suites for Claude-powered agents including assertion patterns and scoring
DeepEval: Open-Source LLM Evaluation Framework Production-ready framework for LLM testing with 14+ metrics including hallucination, toxicity, and coherence
LangSmith Testing and Evaluation Guide Comprehensive guide to evaluating LangChain agents with dataset management, custom evaluators, and regression tracking
Faros AI: The Rise of Coding Agents Analysis of AI agent reliability challenges and emerging testing patterns for autonomous coding systems
Braintrust AI Evaluation Platform Enterprise-grade evaluation patterns including A/B testing prompts, scoring functions, and regression detection