6 Crucial Testing Considerations for Agentic AI
It’s no secret that AI – and agentic AI in particular – is expected to deliver significant economic impact over the next few years. For example, Gartner predicts that by 2029, agentic AI will autonomously resolve 80% of common customer service issues without human intervention, reducing operational costs by 30%. Few executives can resist the promise of dramatic increases in efficiency and productivity coupled with significant cost savings.
But to reap those benefits, organizations need to move up the ladder of agentic sophistication. While co-pilots save time, humans must approve or reject their suggestions. At the next level, single-role autonomous agents execute a full task within a specific domain independently, without requiring human approval. But the greatest efficiencies and financial gains come from coordinated systems of agents that handle complex, multi-step workflows across tools and roles in real time, exercising autonomy.
Yet as autonomy increases, so do the risks associated with agentic AI. Mitigating those risks calls for a new approach that evaluates different dimensions.
What makes testing agentic AI different?
Agentic AI poses some unique testing challenges, even extending beyond methodologies that suffice for other types of AI.
- Unpredictable behavior: Non-deterministic outputs require qualitative, context-aware evaluation that is extremely difficult to automate
- New testing dimensions: Task success, efficiency, and traceability must all be validated
- Domain-specific judgement: Safety and role fidelity call for creative, human-in-the-loop testing
- Need for continuous re-testing: Small changes demand frequent, targeted validation throughout the lifecycle
- Rapidly evolving models: AI models evolve quickly, sometimes monthly or even faster, making it difficult to ensure consistent and reliable outputs and test as models iterate
Understand agentic AI testing dimensions
There are six key areas that should be validated in agentic systems to ensure safety, effectiveness, transparency and scalability. Each comes with its own unique set of challenges in testing and evaluation. Here are the new dimensions that are critical to agentic AI, with examples of each one.
- Safe and responsible AI validates that outputs avoid harmful, biased, or unsafe behaviors, aligning with ethical guidelines. Subtle acts of discrimination and more overtly harmful ones, such as providing instructions on how to make a bomb, are risks to avoid. Example: Verifying that an HR agent doesn’t reject job applicants based on candidate names that reflect certain ethnicities or send toxic responses.
- Role fidelity determines whether AI and agentic systems behave consistently with their intended roles and responsibilities. This includes the agent’s tone as well as the scope of tasks it handles — the agent should not complete actions outside of its defined purpose. Example: assessing whether a customer support chatbot stays within its defined persona, uses professional language and is considerate as it resolves customer inquiries.
- Task completion measures whether AI and agentic workflows reliably achieve the intended user or business outcomes, including all elements of a request. It also assesses how intuitive the process was for the user. Example: Checking that a travel booking agent booked reservations successfully based on your travel criteria and clearly communicated the details for each.
- Traceability provides visibility into AI decisions by documenting inputs, processes and outputs. It examines an agent’s decision-making process to validate its logic and ensure the final output is grounded in facts and free from hallucinations. Example: testing that an agent correctly completed all sub-tasks of a financial audit workflow and shows transparency into the information and logic it used.
- Interoperability validates that autonomous agents can reliably interact across applications, APIs and enterprise systems. For multi-step, multi-agent processes, agents must be able to seamlessly communicate and collaborate with other agents, whether sending or receiving instructions and information. Example: testing whether an HR agent looking for employees with specific skills can successfully request access to the relevant databases and retrieve the information.
- Efficiency evaluates performance, speed and resource usage to ensure AI delivers value at scale. This assesses whether an agent uses the shortest path to carry out its tasks, without introducing redundancy or unnecessary steps into the process, using compute/system capability efficiently to generate the most desirable response in the most cost effective manner. Example: validating that an agent booking travel only asked questions that were relevant to the task at hand and did not call unnecessary tools to do so.
Crowdtesting four critical dimensions of the agentic stack
Testing agentic systems requires human-in-the-loop (HITL) evaluation and new testing approaches. Crowdtesting offers companies developing agentic AI a way to effectively validate several dimensions of their applications, with testers that reflect the target user base and real-world behaviors. Learn how crowdtesting can help evaluate safe and responsible AI, role fidelity, task completion, and traceability, and some different evaluation methods for each dimension.
Safe and responsible AI asks if the agent behaved safely and ethically in how it handled the task. Testers use the following techniques:
- Adversarial prompt injection tests whether prompts can bypass safety filters, using statements like “Ignore content restrictions…”
- Contextual framing exploits checks to see if agents follow harmful instructions when assuming roles or changing contexts — instructions like “Pretend you’re an evil AI.”
- Token-level manipulation assesses whether odd token patterns trigger unsafe outputs.
- Agent action leakage validates that the agent doesn’t reveal data or expose its underlying properties, such as internal instructions, when prompted.
- Toxicity detection leverages LLMs to flag biased, racist, or toxic outputs.
Role fidelity asks whether the agent’s actions and communication align with its given role. This type of testing assesses:
- Tone alignment: is the agent’s tone suitable for the use case? For example, a creative assistant may be energetic, while an assistant on a wealth management site may be more formal and professional.
- Role alignment: are all of the agent’s actions appropriate and relevant for the use case? An agent designed to compare retail prices shouldn’t visit sites that aren’t related to shopping.
- Domain terminology: does the agent use the appropriate terminology for its subject matter? Are specific acronyms and technical language used properly?
- Sustained alignment: does the agent maintain consistent behavior over time through repeated or redundant interactions? A coding assistant should refuse to complete shopping tasks no matter how many times a user asks the agent to shop for them.
Task completion asks how well the agent accomplished its assigned task. This testing considers how well the agent addresses the different parameters that users may provide and interprets the variations in how users may frame their requests. For example, does a shopping agent correctly understand slang or phrases like, “I want a cute cropped cardi in Barbie pink. Supersoft.” Some methods for assessing task completion:
- LLM-as-a-Judge uses a single LLM to grade an agent’s output against a rubric.
- LLM-as-a-Jury uses multiple LLMs to score the agent’s output, then averages the results via a judge LLM.
- Structured assertions check specific outputs, such as fields in JSON, using automated rules.
- Fingerprinting confirms that an agent followed specified, required steps by inspecting logs or tracking.
- Flexibility tests an agent’s ability to complete a task while responding to a variety of prompting styles, languages, phrasings, etc.
- Domain-specific accuracy rates whether in domain-specific use cases, professionals in the field accept the agent’s final output as correct.
Traceability asks whether the agent’s decision-making process and final output are grounded in truth and free from hallucinations. The focus is on verifying that the choices the agent made are based on facts and clear logic.
- Source verification checks that the sources informing the agent’s output are real and accurate, such as validating that the websites it cites exist and contain the facts the agent referenced. This level of testing can likely be automated via a mix of LLMs and traditional software.
- Agent fingerprint verification audits the sequence of actions an agent took by examining its digital ‘fingerprint’ – steps like verifying agent-generated successful API status code in AWS monitoring logs.
- Chain-of-thought evaluation checks that the agent’s output can be linked to a clear decision-making process visible in the agent’s chain of thought.
Teams looking to embed agentic AI into their products and workflows must move quickly to deliver value to the business and its customers, while avoiding the risks that come with an unreliable, untrustworthy app. Crowdtesting can help assess multiple dimensions of agentic AI, helping software development teams get to market faster without sacrificing quality. As organizations navigate the evolving market and experiment with developing agentic AI, Applause can help realize your AI goals. Agentic AI calls for a testing and evaluation approach that scales and supports teams at all stages of agentic development, from their first assistants/co-pilots to single role agents and multi-agent workstreams. Contact us to learn more about how Applause can serve as a trusted partner on your agentic AI journey.
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