LambdaTest Inc., a generative artificial intelligence-powered software testing platform, today unveiled the private beta model release of its AI Agent-to-Agent Testing service, which will allow developers to validate and assess AI agents.

In the short time that AI agents have become a trend, they have become a major facet of digital transformation. Unlike traditional AI chatbots, AI agents can take autonomous actions when provided with goals by breaking them down into step-by-step tasks that require little or no human oversight.

As a result, their behavior is adaptive and unpredictable. That means there’s no standard way to test them to ensure their reliability.

“Every AI agent you deploy is unique, and that’s both its greatest strength and its biggest risk,” said LambdaTest co-founder and Chief Executive Asad Khan. “As AI applications become more complex, traditional testing approaches simply can’t keep up with the dynamic nature of AI agents.”

The rise of agentic AI also means that more enterprise developers are finding themselves working with complex networks of agents that interact across more systems and with each other. Agents are capable of interacting with people in more than just text; they can perform voice calls, read PDFs, look at images, watch videos and interact with computer screens.

LambdaTest said that it developed its proving ground for AI agents to handle these scenarios by allowing teams to upload requirements documents in various formats, including text, images and video. The system handles this analysis to generate test scenarios by simulating real-world challenges that would break the AI agent under stress.

The platform then highlights key metrics such as bias, completeness, hallucinations and so forth. By using these criteria, teams can better analyze agent quality and address their specific needs. The company said the system includes 15 purpose-built testing agents ranging from security researchers to compliance validators.

For example, agentic AI might be deployed in a customer service role where it needs to maintain a casual tone with customers. It must also interoperate with other agents that retrieve information about stocked items and provide rapid response for order updates. Tests might include how well it provides on-brand service, how quickly it responds, its reliability and security.

Reliability and security are of major concern to the enterprise. A 2025 Ernst & Young Global Ltd. survey revealed that 73% of senior leaders believe that one day, entire business units might be managed by agentic AI. Still, this future is hampered by barriers such as fears about cybersecurity, data privacy and managing company policy.

LambdaTest stated that, unlike single-agent testing systems, Agent-to-Agent uses multiple large language models, which serve as the “brains” that AI agents use for reasoning and test generation. This multi-agent approach creates a more comprehensive and detailed test suite, providing teams with a wider variety of alternative and potential edge cases for their AI applications.

“Our platform thinks like a real user, generating smart, context-aware test scenarios that mimic real-world situations your AI might struggle with,” said Khan.

Image: SiliconANGLE/Microsoft Designer