In mid 2024, AI agents became the talk of the tech world—taking on tasks from ordering dinner ingredients to booking flights and appointments. Then came vertical AI agents—highly specialized systems rumored to replace the good old SaaS. But as agents’ influence grows, so does the risk of deploying them prematurely.
An under-tested AI agent can bring a host of issues: inaccurate predictions, hidden biases, lack of adaptability, and security vulnerabilities. These pitfalls confuse users and compromise trust and fairness.
If you're building an AI agent, having a clear roadmap for safely rolling them out is crucial. In this article, we'll explore why careful evaluation is essential, walk through step-by-step testing strategies, and show how SuperAnnotate helps thoroughly evaluate AI agents and ensure their reliable deployment.
Why evaluate AI agents?
Developing an AI agent means preparing it for the unpredictable situations it will face in everyday life. As in the case of LLM evaluation, we want to ensure agents can handle both common tasks and the occasional curveball without making unfair or incorrect decisions. For example, if the agent screens loan applications, it must treat all applicants equally. If it serves as a virtual assistant, it should understand people’s unexpected questions just as well as the routine ones. By thoroughly testing in advance, we can spot and fix potential issues before they cause real harm.
Evaluation is also crucial for meeting regulations and earning trust. Certain fields, like finance and healthcare, have strict rules to protect people’s privacy and safety. Demonstrating that an AI tool meets these standards reassures regulators, stakeholders, and users that it’s been properly vetted. People are more likely to trust a system—and let it make important decisions—when they see evidence that it’s undergone realistic, thorough testing.
Finally, ongoing evaluation helps keep your AI agent in top form as conditions shift over time. Even if it works well in a controlled environment, the world keeps changing. Regular testing lets us catch any performance slowdowns, overlooked scenarios, or subtle new biases. With each update, the agent becomes more effective, delivering reliable results under a wider range of conditions.
How to evaluate an AI agent?
Evaluating an AI agent doesn’t have to be overly complex, but it does need to be methodical. Below is a practical approach to designing and running agent evaluations.
1. Build a thorough test suite
Agent evaluation should start by gathering a wide range of example inputs that reflect both the typical and trickier ways users might interact with your agent. You don’t need an overwhelming number of cases; aim for coverage over quantity. For example, if you’re creating a chatbot for customer support, make sure to include:
- Normal inquiries (like “Where’s my order?”)
- Edge cases (like random off-topic questions or heavily phrased requests)
- Queries that target specific functions your agent can perform
Over time, expand or modify this set as you see new usage patterns.
2. Outline the agent’s workflow
Next comes breaking down the agent’s internal logic. Each significant step—whether it’s calling a function, using a skill, or making a routing decision—deserves its own evaluation. By mapping out every path the agent can take, you can better pinpoint where issues might appear.
3. Pick the right evaluation methods
With a clear view of your agent’s steps, decide how to measure them. Generally, there are two main strategies:
- Compare to an expected outcome
If you can specify the result you want in advance—say, a known piece of data—then you can match the agent’s output against that expectation. This approach helps you quickly see if something’s off. - Use another model or heuristic
When there’s no definitive “correct” answer or when you want qualitative feedback (e.g., how natural a response sounds), involve another language model – LLM-as-a-judge, or a manual reviewer to judge. This approach is less rigid but can give more nuanced insights.
4. Factor in agent-specific challenges
Beyond testing the individual pieces, look at how the agent pulls everything together:
- Skill selection: If the agent chooses from multiple functions, you need to confirm it selects the right one each time.
- Parameter extraction: Check that it not only picks the correct skill but also passes the right details along. Inputs can be complex or overlapping, so thorough test cases help here.
- Execution path: Make sure the agent isn’t getting stuck in unnecessary loops or making repetitive calls. These flow-level problems can be particularly tough to track down.
5. Iterate and refine
Finally, once everything is set up, you can begin tweaking and improving your LLM agent. After each change—be it a prompt revision, a new function, or a logic adjustment—run your test suite again. This is how you track progress and catch any new glitches you might introduce.
Keep adding new test scenarios if you spot fresh edge cases or if user behavior shifts. Even if that means your newer results aren’t directly comparable to older runs, it’s more important to capture real-world challenges as they emerge.
Sample AI agent
Suppose you want an agent to book a trip to San Francisco. What goes on behind the scene?
- First, the agent has to figure out which tool or API it should call based on your request. It needs to understand what you’re really asking for and which resources will help.
- Next, it might call a search API to check available flights or hotels, and it could decide to ask you follow-up questions or refine how it constructs the request for that tool.
- Finally, you want it to return a friendly and accurate response ideally with the correct trip details.
AI agent evaluation example
Now let's think about how you'd evaluate this step-by-step.
There are a few things to check. Did the agent pick the right tool in the first place? When it forms a search or booking request, does it call the correct function with the right parameters? Is it using your context, for instance the dates, preferences, and location accurately? How does the final response look? Does it have the right tone, and is it factually correct?
In this system, there’s plenty that can go wrong. For example, the agent might book flights to San Diego instead of San Francisco. That’s why it’s not only important to evaluate the LLM's output, but also how the agent decides on each action. You might find that the agent is calling the wrong tool, misusing context, or even using an inappropriate tone. Sometimes users will also try to manipulate the system, which can create unexpected outputs. To evaluate each of these factors, you can use human feedback, human-in-the-loop, or LLM-as-a-judge to assess whether the agent's response truly meets your requirements.
Evaluate AI Agents with SuperAnnotate
Evaluating agent-based systems can be challenging, but SuperAnnotate’s customizable interface gives you a clear view into every step—whether you’re analyzing data inputs, decision paths, or tool use. By simplifying dataset creation and providing performance insights, SuperAnnotate helps you identify exactly where your agent might be struggling and how to improve it.
Adapt to your agent setup
SuperAnnotate’s flexible UI adjusts to your workflows, making it easier to visualize each stage of your agent’s reasoning. You can see which skills or tools the agent used, how decisions were made, and where any missteps occurred.
Seamless data integration
Direct integration with your data and AI platforms allows you to import critical information—like agent decisions, function calls, and final responses—right into SuperAnnotate. Having these details in one place streamlines your evaluation process, cuts down on busywork, and speeds up your ability to implement improvements.
Collaborative workflows
SuperAnnotate is designed for teamwork, whether you’re involving subject-matter experts or using LLMs as evaluators. Multiple reviewers can weigh in on agent outputs, add annotations, and flag areas for revision. By bringing diverse perspectives together, you ensure thorough and balanced evaluations.
Data security
With SOC2 Type 2 and ISO27001 certifications, SuperAnnotate protects your data both in the cloud and on-premises. Role-based access controls and data segmentation further safeguard sensitive information, so your teams can focus on building better agents with peace of mind.
Agent evaluation example with SuperAnnotate
To make this concrete, imagine a basic planning multi-turn agent tasked with organizing an 80s-themed party. We’ll use SuperAnnotate’s platform to run the agent and collect an evaluation dataset based on user’ preferences.
As it gathers user preferences, the agent can ask follow-up questions, recommend different activities, and adjust its suggestions to match what you’re looking for. Here, we’ll focus on four evaluation criteria: relevance, usefulness, factual accuracy, and the diversity of its suggestions.
For the sake of simplicity, we’ll only show one round of conversation in this example. Ideally, the agent would take multiple turns, refining its suggestions and questions until the result is fully aligned with the user’s expectations.
As an example, the agent might propose multiple follow-up questions about music, outfits, decorations, or party games. It might suggest encouraging guests to dress in neon hues, wear leg warmers or shoulder pads, and use eye-catching 80s-themed invitations.
After gathering enough details, here’s a sample plan the agent generates based on those follow-up questions and suggestions.
Collecting evaluation data helps align the agent’s actions with user preferences. Over time, this data-driven refinement process helps the agent offer recommendations that become increasingly relevant, accurate, and diverse
Final thoughts
Thorough evaluation is the backbone of a trustworthy AI agent. By testing each step of its workflow, gathering real-world feedback, and making precise refinements, you create a reliable system that people can count on. Whether it’s booking flights or handling customer inquiries, a well-evaluated agent consistently delivers accurate, helpful results—ensuring it remains aligned with user needs every step of the way.
