Agentic AI Testing

Did the agent behave safely and ethically in how it handled the task?

As part of our comprehensive approach, we employ red teaming, an AI best practice that exposes potential vulnerabilities to threats, including bias, racism and malicious intent through adversarial testing. With red team engagements, Applause can assemble diverse teams of trusted testers to “launch attacks” and uncover issues, testing both agent communications and actions for harmful behaviors and weaknesses. These engagements can include: adversarial prompt injections to test if prompts can bypass safety filters, contextual framing exploits to check if agents are following harmful instructions when assuming roles or changing contexts, token-level manipulation to validate whether odd token patterns trigger unsafe outputs, agent action leakage to prevent an agent from revealing data or exposing its underlying properties when prompted, or toxicity detection to leverage LLMs to flag biased, racist or other toxic outputs.

Example: Testing that a travel booking agent does not agree to requests for instructions to make a bomb.

Did the agent's actions and communication align with its given role?

We leverage human expertise to analyze agent performance. As part of a systematic approach to evaluating the accuracy and quality of agent responses, we can check for the following: tone and role alignment to validate that an agent's tone and actions are suitable for its use case; domain terminology to validate whether agents use the correct terminology, acronyms and professional language within a specific domain; and check for sustained alignment to test that the tone and role are consistent across repeated and redundant interactions.

Example: Testing that a travel booking agent keeps a professional tone and does not take non-booking-related actions.

How well did the agent accomplish the task it was given?

For this testing, Applause helps ensure agents can successfully perform tasks across a wide range of real-world conditions. To evaluate flexibility, testers simulate diverse prompting styles – varying language, dialects, typos, and shorthand – to assess adaptability. Expert reviewers validate domain-specific accuracy in fields like finance or science. We also assess human interaction quality to see how real users experience the agent – testing clarity of prompts, perceived helpfulness, trust or satisfaction (e.g., NPS, CSAT), and how agents handle errors or bad input. These human-led evaluations go beyond automated metrics to ensure agentic experiences are not just functional, but intuitive, trustworthy, and ready for real-world deployment.

Example: Testing that an agent correctly booked travel details and communicated them clearly to a user.

Is the agent’s decision-making process and final output grounded in truth and free from hallucinations?

Source verification and chain-of-thought evaluation are critical for detecting hallucinations in agent responses. These evaluations assess whether cited sources are legitimate and whether the reasoning process is leading to a sound decision, such as choosing the cheapest itinerary. While some checks can be automated without relying on LLMs, others require human judgment to ensure accuracy and reduce hallucination risk. Since agents inherently depend on LLMs – even during testing – they remain vulnerable to generating plausible-sounding but incorrect information. Applause testers play a key role in verifying that references are real, relevant and appropriately used, and that the agent’s reasoning aligns with the correct decision path.

Example: Testing that an agent correctly completed all sub-tasks of a packaged travel purchase workflow

Did the agent make cost-efficient use of both reasoning and actions?

To ensure AI agents operate cost-effectively, it's critical to evaluate not just the correctness of their outputs, but also the efficiency of their reasoning and actions. A crowdtesting partner like Applause can support client teams in validating an agent’s efficiency across multiple levels – including trajectory-level efficiency, user interaction-level efficiency, and single-step efficiency. We can help identify redundant or unnecessary steps in the overall trajectory of an interaction, detect excessive back-and-forth with end users that may indicate friction or inefficiency, and see if prompts can be streamlined without degrading agent performance. By testing these layers in real-world contexts with human feedback, Applause helps organizations fine-tune agents for both smarter decision-making and lower operational costs.

Example: Testing that an agent did not take unnecessary steps when booking travel and did not have to iterate excessively with user

Can the agent reliably interact with other agents?

As multi-agent systems and orchestration frameworks continue to scale, interoperability testing is becoming increasingly important – though still in its early stages. These tests help ensure that agents can seamlessly communicate and collaborate with other agents, whether by handling task management – receiving and executing instructions from orchestration layers like Model Context Protocol (MCP) – or by initiating task requests to external agents, passing along the correct context or content. Applause can help you validate whether agents correctly interpret, execute and respond to external agent instructions in real-world conditions. As agent ecosystems grow more complex, ensuring smooth agent-to-agent interaction will be essential to delivering scalable, reliable AI-powered solutions.

Example: Testing whether a booking agent can interact with a site that exposes a shopping agent based on MCP1.