What are AI evals? A guide to AI model & agent evals

As AI models and agents take on more complex professional work, knowing how they perform before deployment is critical. For AI research teams and enterprise AI leaders, the challenge goes beyond measuring capabilities to identifying failure modes, assessing reliability, and determining whether AI systems are ready for real-world use.

So how can teams evaluate performance effectively and make more informed decisions about the models and agents they build or deploy? That's where AI evals come in. This guide explores what they are, why they matter, how they work, the different types of evaluations, and what makes them effective.

What are AI Evals?

AI evals (short for evaluation) are structured tests that measure how well an AI model or agent performs against defined criteria. They help teams identify strengths, uncover weaknesses, and determine whether an AI system can reliably handle its intended tasks.

Common evaluation criteria include accuracy, relevance, completeness, instruction following, reliability, and task completion.

For specialized tasks requiring professional judgment, human subject matter experts (SMEs) can help define evaluation criteria and assess whether results meet real-world professional standards.

Why are AI evals critical for AI teams?

AI evals have become increasingly important because AI systems behave differently from traditional software. Their outputs can vary even when given the same input, and many tasks have multiple acceptable answers, making performance harder to measure with simple pass-or-fail tests. AI agents add another layer of complexity, since they may reach the correct outcome despite flawed reasoning or improper tool use, so evaluating the final result alone doesn't tell the whole story. Meanwhile, as models improve, widely used public benchmarks can become saturated, making it harder to distinguish meaningful differences in capability.

Effective AI evals address these challenges by testing models and agents against realistic tasks, using clear success criteria, and examining both outcomes and execution. This gives research teams and enterprise AI leaders a more reliable way to measure progress, identify weaknesses, and assess readiness for real-world deployment.

5 key benefits of AI evals for product development

Organizations using AI evals for product development enjoy a few different benefits, ranging from measurable standards to quality at scale.

1. Establish measurable standards

AI evals help teams define what successful behavior looks like, even when tasks have multiple acceptable answers. Product managers can translate user needs into evaluation criteria, while engineers can use those criteria to test whether the system behaves as intended.

2. Improve reliability and prevent regressions

Regular evals help teams catch errors and inconsistent responses, and flag unexpected behavior before it affects users. They also make it easier to spot regressions when a model, prompt, or system component changes.

3. Accelerate development and continuous improvement

By identifying where an AI system falls short, evals give teams a clear starting point for making improvements. Automated testing also helps teams assess changes more quickly without the need for manual reviews at every iteration.

4. Make data-driven model and product decisions

Eval results help teams compare models, measure the impact of changes, and decide which solutions best meet their needs. Instead of relying on anecdotal feedback or assumptions, teams can use performance data to make decisions.

5. Align teams and maintain quality at scale

Shared evaluation criteria give product, engineering, and business teams a common understanding of what good performance looks like. As AI products grow, consistent evals help teams maintain quality across features, use cases, and model updates.

What do AI evals measure?

Depending on the use case, evals may assess accuracy, output quality, reliability, groundedness (whether responses are supported by evidence), safety, policy compliance, latency, and cost.

For complex professional tasks, evals may also measure task completion, decision-making, and adherence to domain-specific standards to determine whether an AI system can perform reliably in real-world applications.

To explore these measures in more detail, read Mercor's guide to AI evaluation metrics.

What are the different types of AI evals?

Just like workflows, AI evals may take various forms, depending on what a team needs to measure. Common types of AI evals include:

• Benchmark evals: An AI model's performance is compared against a standardized set of tasks or questions. Benchmarks help teams measure capabilities consistently and compare performance across different models.
• Automated evals: Using code, predefined rules, or AI-based graders, outputs are scored quickly and consistently. This method is useful for testing large volumes of responses and measuring performance across repeated evaluation cycles.
• Human evals: These rely on experts to assess AI outputs for qualities such as accuracy, clarity, relevance, and professional judgment. They are especially valuable when a task requires contextual understanding or specialized knowledge that automated scoring may miss, such as legal, finance, engineering, or medical evaluation from human SMEs.
• Rubric-based evals: Rubrics help make evaluations more consistent and transparent, particularly for complex tasks where there may be several acceptable answers or approaches. Predefined criteria and scoring guidelines assess outputs across multiple dimensions and can be applied by human reviewers or automated graders.
• Model evals: These measure how well an individual AI model performs specific tasks, such as answering questions, generating code, or solving problems. They help teams identify strengths and weaknesses and determine whether a model meets the requirements of a particular use case.
• Agent evals: Particularly useful for determining whether an agent can successfully perform complex tasks in realistic conditions, agent evals are designed to complete multi-step tasks using tools, external systems, or feedback from their environment. In addition to the final result, evaluations may measure task completion, tool use, decision-making, and reliability throughout the workflow.

What is the difference between online and offline AI evals?

Offline evals provide a controlled way to test capabilities against defined tasks and criteria before an AI model or agent is deployed and reaches real users.

Because these evaluations take place in a controlled environment, teams can measure performance against established criteria, compare and contrast different models, identify weaknesses, and test if a system is truly ready for release.

Online AI evals, by contrast, assess systems in live (or production) environments using real-world interactions, user feedback, and operational performance data. While offline evals focus on predicting performance before launch, online evals help teams understand how a system performs in actual use.

Both online and offline AI evals play an important role in developing reliable AI systems. Offline evals are crucial for helping teams identify problems before deployment, while online evals can reveal issues that emerge in real-world conditions. Mercor’s focus on offline evals uses expert-designed tasks and evaluation criteria to measure AI models and agents against realistic professional work before deployment.

Through its APEX methodology, Mercor helps AI teams assess capabilities in a controlled, structured environment before a model ships. These expert-built tasks and rubrics provide insights into model performance before systems ever reach users.

What is the structure of an AI eval?

At its core, an eval is simply a test with a given input and an expected output based on logic. In early LLMs, these evals began as a single-turn evaluation with one prompt, response, and logical grade as the main eval. With advancements, multi-turn evaluations have become the norm, evaluating the system across a series of interactions rather than a single response.

Single-turn evaluation

A single-turn eval tests an AI model's response to one prompt or input. The response is then scored against predefined criteria, such as accuracy, relevance, or whether it meets an expected output. These evaluations work well for tasks with a clear, self-contained answer, such as answering a factual question or summarizing a document.

Multi-turn evaluations

Multi-turn evals assess how well an AI system performs across a series of interactions. They measure whether it maintains context, responds to new information, and builds on earlier exchanges rather than treating each prompt as a separate task.

Agent evaluations

Agent evals go a step further by testing an AI agent's ability to complete a task through multiple actions, such as using tools, interacting with applications, and making decisions. They assess the full workflow, including whether the agent follows instructions, uses tools appropriately, and achieves the intended outcome. While agent evals often involve multiple turns, not every multi-turn eval is an agent eval.

Both types of evaluations require clear success criteria and a consistent scoring method, which may involve predefined rules, automated grading, or human expert review. The goal is to determine whether the AI system performs accurately and reliably under the conditions it is likely to encounter in practice.

How do AI evals work?

At their core, AI evals consist of 5 core components that help teams measure how well an AI model or agent performs against defined expectations. Each component plays a role in producing meaningful results and identifying areas for improvement.

1. Test case/task: Each evaluation begins with a specific task the AI system is expected to perform. Test cases should reflect realistic scenarios the system may encounter, using representative inputs, instructions, or situations to test its capabilities.
2. Expected behavior/success criteria: Before running the test, teams define what a successful result looks like. These criteria might include factual accuracy, following instructions, completing a task correctly, or meeting specific professional standards. Clear expectations make it easier to assess performance consistently.
3. AI execution/trial: The AI model or agent receives the test input and attempts to complete the task. Depending on the evaluation, this could involve generating a single response, handling a series of interactions, or completing a multi-step workflow using tools and external applications. The system's outputs and actions are recorded for evaluation.
4. Grading logic: Once the AI completes the task, its performance is scored against the established criteria. Grading may use automated checks, predefined rules, another AI model acting as a grader, or human SMEs. The approach depends on the task and whether success can be measured objectively or requires professional judgment.
5. Analyzing performance: The results are reviewed to identify patterns, strengths, weaknesses, and recurring failure points. Teams can compare performance across models or versions, determine which changes improve results, and use those findings to refine the system. Repeating this process helps teams track progress and make more informed decisions about deployment readiness.

Together, these elements create a consistent, measurable way to conduct AI evaluations and gauge performance. Each step helps ensure the AI eval reflects real-world tasks, uses meaningful success criteria, and produces results that teams can use to identify weaknesses and make improvements. Without this structured approach, it's difficult to determine whether a model is genuinely improving or is simply producing different outputs.

How do Agent evals work?

For agent evals, the underlying structure remains the same, but the evaluation is more complex because agents can perform multiple steps, call external tools, and modify their environments. Agent evals contain two more core components not found in AI evals:

Trace

A trace records the sequence of steps an agent takes to complete a task, including its decisions, tool calls, and interactions with external systems. Reviewing the trace helps teams understand how the agent approached the task, identify errors along the way, and determine where its process could be improved.

Outcome

The outcome measures what the agent actually accomplished and whether it achieved the task's intended goal. This may involve checking the final result, verifying changes made to an environment, or confirming that specific requirements were met. Evaluating the outcome helps teams distinguish between an agent that follows a reasonable process and one that successfully completes the task.

What makes an AI eval effective?

A good AI eval measures how well a model or agent performs tasks it will actually encounter in practice. Since a high score on a benchmark test doesn't necessarily mean a system can handle the work a business needs it to do, to produce useful results, an effective AI eval should meet several criteria:

• Representative tasks: Test scenarios that reflect real-world use cases and the complexity of the work.
• Clear success criteria: Define what a successful result looks like before running the evaluation.
• High-quality test data: Use accurate, relevant inputs that reflect the range of situations the system may encounter.
• Consistent scoring: Apply reliable grading methods, whether through automated checks, predefined rubrics, or human review.
• Relevant metrics: Measure the capabilities that matter for the task, such as accuracy, reliability, task completion, or professional output quality.
• Domain expertise: Involve subject matter experts when evaluating work that requires specialized knowledge or professional judgment.

Identifying the right AI eval metrics for your company depends on what the AI system is expected to do. A useful evaluation goes beyond a single score to assess the capabilities that matter in real-world applications, using clear criteria tailored to each use case and expert judgment when specialized knowledge is needed.

How Mercor supports effective AI evaluations

For complex professional work, automated evaluations alone may not capture every dimension of quality. Human subject matter experts can help define what successful performance looks like, develop evaluation rubrics, and assess results requiring specialized knowledge or judgment.

Mercor connects AI teams with domain experts who bring this expertise into the evaluation process, helping teams measure performance against real-world professional standards and identify areas for improvement.