Introducing Agent to Agent Testing

A unified platform to test AI agents, including chatbots and voice assistants, across real-world scenarios, ensuring their accuracy, reliability, efficiency, and performance.

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A unified platform to test AI agents, including chatbots and voice assistants, across real-world scenarios, ensuring their accuracy, reliability, efficiency, and performance.
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An AI Agent for Testing AI Agents

As AI agents grow more complex, validating their accuracy, reliability, efficiency, and performance becomes challenging. LambdaTest is the first platform where intelligent agents validate other AI agents, enabling testing at the same level of complexity as the systems under test.

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An AI Agent for Testing AI Agents

Autonomous Test Generation at Scale

Validate AI agents across text, voice, or hybrid interactions, covering diverse cases and security gaps. Boost coverage 5-10x while ensuring consistent flows, intent, tone, and reasoning, with advanced checks like risk scoring and behavior validation beyond traditional methods.

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Agentic Testing Platform

True Multi-Modal Understanding

Go beyond text! Upload requirements from various formats such as images, audio, and video. This broader context ensures a deeper grasp of the intended behavior, resulting in tests that are more accurate, relevant, and impactful.

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Agent to Agent Testing Platform

Automated Multi-Agent Test Generation

Leverage a team of specialized AI agents to generate diverse, context-rich test scenarios, creating a high-quality test suite that mirrors real-world interactions and edge conditions. Agentic AI and GenAI ensure more varied, expert-driven test cases compared to a single general-purpose agent.

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Agentic Testing Platform

Automated Test Scenarios

Generate comprehensive test scenarios across multiple categories, ensuring thorough validation of your conversational AI systems and applications.Test scenarios auto generated as per different categories such as intent recognition, conversational flow with several validation criteria.

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Agentic Testing Platform

Seamless Integration with HyperExecute

Our platform seamlessly integrates with LambdaTest’s HyperExecute for large-scale cloud execution. Generate test scenarios and run them at scale with minimal setup, delivering actionable feedback in minutes.

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HyperExecute

Actionable Insights

Assess test results with customizable response schemas or sample outputs for clear, categorized insights. Make data-driven decisions on agent performance and optimization by evaluating key metrics like Bias, Completeness, and Hallucinations, ensuring relevance, accuracy, and efficiency.

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Actionable Insights

Customer Success Stories

World's leading companies trust LambdaTest with their digital transformation journey.

70%

Faster Test Execution

24/7

Top-Notch Customer Support

Comma

LambdaTest helped us achieve faster time-to-market and enhanced CX.

Daniel de Bruijn

Quality Assurance Automation Engineer

transavia
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Frequently Asked Questions

What is agent to agent testing?
Agent-to-agent testing refers to a testing approach where two or more AI agents interact with each other in a controlled environment to simulate real-world scenarios. This type of testing is used to evaluate how AI systems or agents, such as chatbots or virtual assistants, perform when they communicate or work together. The goal is to test their ability to understand, react to, and collaborate with each other in complex, dynamic environments, ensuring they can operate effectively in various use cases.
What is a testing agent?
An agent is a software component or module that functions autonomously to carry out specific testing tasks. These tasks may involve executing test scripts, gathering data, and generating reports.
What is agentic testing?
Agentic testing leverages autonomous AI agents to autonomously generate, execute, and refine tests throughout the entire software testing lifecycle.
What are the tech stack powering these AI capabilities for Agent-to-Agent Testing? Are these your or third-party models?
The AI capabilities are powered by a combination of third-party models and a sophisticated in-house agentic framework. Core AI Models: The system is primarily built upon multiple large language models (LLMs). The agents use these models for their core reasoning and generation tasks.
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