How to evaluate AI agents: A complete guide

AI agents are autonomous software systems that reason, plan, and use tools to achieve a goal. Because they interact with other systems and take actions across multiple steps, it's essential to evaluate the agent's entire workflow rather than just the final output.

This guide explains what AI agent evaluation is, which frameworks, methods, and metrics to use, and how to build a practical assessment process with representative tasks, realistic environments, reliable graders, repeated testing, failure analysis, and deployment criteria.

What is AI agent evaluation?

AI agent evaluation is the process of testing whether an agent can complete a defined workflow correctly, reliably, efficiently, and safely in the environment where it will be used. It evaluates the entire system, not just the underlying model. That includes the prompts, tools, memory, permissions, context, and any external systems the agent can access or modify.

AI agents work toward goals through multiple steps, such as planning, calling tools, reading files, and updating records. The amount of independence they have can vary.

A contract-review agent, for example, must do more than produce a plausible summary. It must use approved sources, preserve citations, follow escalation rules, and create the required output without making unauthorized changes. This illustrates what AI evaluation measures: not just output quality but whether an agent can reliably execute a workflow within the required rules and constraints.

How evaluating AI agents differs from testing AI models

Model evaluation shows what a model can do under defined test conditions. Agent evaluation goes further by testing whether that capability still holds across real-world workflows. As NVIDIA’s agent evaluation guide explains, agentic systems should be evaluated based on outcomes, tool use, intermediate steps, and reasoning, with the final response treated as only one part of the assessment.

The differences become clearer when you compare how each type of evaluation looks in practice:

• Static response vs. completed workflow: Model tests score an answer to a single input, while agent tests assess whether a multistep task reaches the correct result.
• Isolated model vs. complete system: A model score doesn’t capture failures caused by prompts, tools, memory, permissions, or the surrounding environment.
• Final answer vs. full process: Agent evaluation also looks at tool selection, intermediate actions, error recovery, generated outputs, and changes made to external systems.
• One-shot score vs. repeated evidence: Because agent behavior can vary across runs, teams need to conduct repeated tests rather than relying on a single good result.

A strong model is only one factor in an agent’s performance. What often matters more is whether the complete agent system can perform reliably under the required workflow conditions.

AI agent evaluation frameworks, methodologies, and metrics

An effective AI agent evaluation framework separates the process into three parts. The approach identifies who or what judges performance, the metric defines what gets measured, and the methodology explains how the test is carried out. Keeping these roles separate helps teams avoid treating a large language model (LLM) as the ultimate authority or as a complete assessment process on its own.

High-stakes professional workflows also need standards that reflect the specific work being tested. What counts as a correct result depends on the final output, the steps the agent takes, and the consequences of an error. Teams should adjust their evaluation methods based on how clearly success can be measured and how much risk is involved.

Primary agent evaluation frameworks

Teams typically use three main approaches to evaluate AI agents:

• Automated evaluation: Automated checks and model-based graders can score many runs quickly. They work well for requirements such as file presence, valid formats, calculations, citations, tool calls, and other results that can be checked directly. However, because automated graders can be inconsistent or biased, they should support broader testing rather than being relied upon to make high-stakes decisions on their own.
• Expert human evaluation: Domain professionals use clear rubrics to review complex work, such as legal analysis, medical research, financial modeling, or client recommendations. Expert human review is especially useful when professional judgment matters; however, it requires more time and resources.
• Hybrid evaluation: Automation handles repeatable checks at scale, while experts define standards, review uncertain cases, resolve disagreements, and validate model-based graders. This approach combines efficiency with the professional judgment needed for higher-stakes evaluations.

Common AI agent evaluation methodologies

Teams can use several evaluation methods to measure agent performance under different conditions:

• Golden-task suites: Fixed task sets measure repeatable performance against an expected, ground truth output defined by a human.
• Sandboxed workflow simulations: Simulated environments reveal tool and environment failures without risking live systems.
• Repeated controlled trials: Repeated runs show how much performance varies across trials. The number of trials should reflect the consistency of the results, how much certainty is needed, how common the task is, and the seriousness of a potential failure.
• Process replay and failure analysis: These methods help teams identify where an agent’s plan, tool use, or recovery process failed. They work best when the evaluation records sufficient detail about each step.
• Adversarial and edge-case testing: Such checks examine how the agent handles misuse, prompt injection, excessive permissions, and other unusual or risky situations. OWASP guidance for LLM applications provides a useful security-focused starting point.
• Shadow and staged pilots: Limited-scale testing shows how an agent behaves before broader deployment. Teams should still have rollback plans and clear protocols for human escalation.
• Continuous regression testing and monitoring: Ongoing testing and monitoring help detect changes after deployment, especially when new incidents and failure cases are added to the test suite.

Important AI agent evaluation metrics

Each metric should clearly define what is being measured, how success is calculated, and what threshold the agent must meet. Common metrics include:

• Task success: The percentage of runs where the final output meets all required criteria.
• Process quality: Consistency in following an acceptable plan, using reliable information, and recovering from errors.
• Tool accuracy: How often the agent selects the right tool, uses valid inputs, successfully completes the action, and avoids unauthorized changes.
• External changes: Whether the agent completes required updates to external systems without making unauthorized changes.
• Repeatability: How consistently the agent passes repeated tests across different tasks and levels of difficulty.
• Risk failures: The number and severity of safety, permission, privacy, or compliance failures. Serious failures should prevent approval.
• Operational performance: The speed, cost, token use, and number of tool calls required to successfully complete a task.
• Expert acceptance: The percentage of outputs approved by qualified reviewers using a documented rubric.

Teams shouldn’t combine all of these metrics into a single average score. Strong overall performance can’t make up for a serious safety, privacy, or compliance failure.

How to evaluate AI agents: A step-by-step guide

A useful agent evaluation starts with the work the agent is expected to perform, not the model powering it. The goal is to recreate enough of that work to see whether the agent can complete it reliably, use the right tools and information, stay within its boundaries, and recover when something goes wrong.

That requires more than assembling a benchmark and calculating an average score. Teams need representative tasks, a realistic environment, explicit success criteria, trustworthy graders, and repeated trials that reveal where performance holds up and where it breaks down.

1. Define the workflow and evaluation objective

Start by describing the job you are actually asking the agent to do. “Evaluate our research agent” is too broad because it leaves success open to interpretation. A useful evaluation objective specifies the workflow, the expected outcome, and the conditions the agent has to operate within.

For example, instead of asking whether an agent is “good at competitive research,” define the objective as: Can the agent produce a client-ready competitive research brief using approved sources while meeting defined standards for accuracy, citations, latency, and cost?

At this stage, establish the basic boundaries of the workflow:

• Who will use the agent?
• What task or process should it complete?
• What does the desired end state look like?
• Which systems, tools, and data can it use?
• What is explicitly outside its scope?
• Where is human review required?

A more concrete definition makes later assessment decisions easier. It determines which tasks belong in the test set, what the environment needs to reproduce, and what should ultimately count as a pass or failure.

2. Identify the failures that matter before you start testing

Not every mistake should carry the same weight. An agent that overlooks a useful secondary source has failed differently from one that fabricates evidence, exposes private information, or modifies a system it was never authorized to touch.

Define those distinctions before you see the results.

Start by identifying failures that would make a run unacceptable regardless of how good the rest of the output appears. Depending on the workflow, these might include unauthorized changes, privacy breaches, use of restricted information, prohibited tool calls, fabricated evidence, missed escalations, or actions outside the agent's permissions.

This matters because aggregate scores can otherwise hide consequential behavior. An agent could complete 95% of a process correctly and still be unsuitable for deployment if the remaining 5% includes a failure the organization cannot tolerate.

Think of these rules as the guardrails around the evaluation. They prevent teams from moving the goalposts after seeing an impressive demo or a strong headline score.

3. Record exactly which agent you are evaluating

Agent performance belongs to a configuration rather than simply to a model name.

Changing the system prompt, available tools, permissions, retry policy, memory, execution budget, or orchestration layer can materially change how the same underlying model behaves. If those details are not recorded, it becomes difficult to reproduce a result or understand why performance changed between evaluations.

For every test, document the configuration that could influence the agent's behavior, including the:

• Model and version
• System prompt and agent instructions
• Available tools and tool definitions
• Agent harness or orchestration setup
• Memory and accessible context
• Permissions and safeguards
• Retry behavior and maximum steps
• Token, time, and cost limits

Recording these details becomes especially important when comparing two agents. You want to know whether one configuration is genuinely better or whether it simply has more tools, more context, broader permissions, or a larger execution budget.

Treat the configuration as part of the evaluated system.

4. Build an evaluation set that resembles the real work

A good evaluation set is not simply a collection of difficult prompts. It should reproduce the range of situations the agent is likely to encounter once people actually use it.

Start with the work itself. Include routine cases the agent should handle consistently, moderately difficult tasks that require judgment, and harder tasks that stretch planning, tool use, or recovery. Then add the cases most likely to expose weaknesses: ambiguous instructions, incomplete information, unusual inputs, long-horizon tasks, historically difficult examples, and rare but consequential situations.

For a contract-review agent, a useful test suite might contain standard vendor agreements alongside contracts with missing clauses, unusual liability provisions, conflicting language, and cases that require escalation.

The quality of the evaluation tasks also matters. Before treating an agent failure as meaningful, confirm that the task is clear, solvable, and supported by the information the agent is supposed to use. Where possible, maintain reference evidence or a known-good solution so evaluators can distinguish a genuine agent failure from a broken test.

Finally, separate tasks used during development from holdout tasks used for assessment. If the agent has repeatedly been optimized against the same examples, high performance on those examples tells you less about how well it will generalize.

5. Give the agent a realistic environment to work in

An agent can look capable when the environment removes all of the difficult parts of the job.

Evaluating a software-engineering agent on isolated code snippets, for instance, tells you much less than placing it in a repository where it has to find the relevant files, understand dependencies, make a change, run tests, respond to failures, and leave the repository in a valid state.

Build enough of the real environment to expose the failure modes you care about. That might involve files, repositories, spreadsheets, databases, browsers, APIs, ticketing systems, document libraries, or simulated business applications.

You do not need to recreate the entire production stack. You need a test environment realistic enough that the agent has to navigate the same kinds of dependencies and constraints it will face in practice.

For agents that can take consequential actions, begin in a sandbox or simulation. The environment should also be reset or otherwise controlled between trials so that one run does not silently change the starting conditions for the next.

This environment, along with the machinery that launches tasks, records actions, resets state, and invokes graders, becomes the evaluation harness. A reliable harness is what turns one-off testing into a repeatable process.

6. Define what success looks like before judging the results

Once the tasks and environment are in place, define exactly what a successful run requires.

For agents, success usually has several layers. The final outcome matters, but so do the actions used to reach it. A contract-review agent may identify the right risk while still failing the evaluation because it relied on an unapproved source or edited a document it was only permitted to review.

A practical rubric can separate requirements into:

• Outcome: What must ultimately be accomplished?
• Evidence: What information must support the result?
• Process: Are there required or prohibited steps?
• Tool use: Which tools or systems may be used, and how?
• Quality: What standard must the final deliverable meet?
• Hard failures: What immediately makes the run unacceptable?

For a contract-review agent, success might require identifying all material issues, supporting them with the correct clauses, consulting the approved clause library, and escalating defined cases. An unauthorized contract modification could automatically fail the trial.

Avoid criteria such as “good reasoning” or “high-quality answer” when you can describe the requirement more precisely. A strong rubric should make it possible for two evaluators to look at the same run and reach roughly the same conclusion.

7. Choose the right grader for each requirement

Not every part of an agent evaluation should be judged the same way.

Use deterministic checks whenever the requirement can be verified directly. If you can inspect database state, validate a schema, run a unit test, compare a calculation, confirm that a file exists, or check whether an unauthorized action occurred, code is usually a more reliable grader than another language model.

Use model-based graders when the requirement involves semantic judgment, such as completeness, clarity, relevance, or whether an analysis satisfies a written rubric. Expert reviewers are most valuable when the work depends on professional judgment or when the consequences of getting the grade wrong are high.

In practice, strong evaluations often combine all three.

The important step is to test the graders before trusting them at scale. Have qualified reviewers grade a representative sample, run automated or model-based graders on the same cases, and inspect where their judgments diverge. Systematic disagreement may point to a weak rubric, an unreliable grading prompt, or a criterion that should not have been automated in the first place.

The grader is part of the testing system. If it cannot reliably recognize success and failure, the resulting benchmark will not be trustworthy no matter how many trials you run.

8. Run the same tasks more than once

Agents are not deterministic systems. The same agent can take different paths, call different tools, or arrive at different answers when given the same task more than once.

That makes repeated trials essential.

For the initial comparison, keep the important conditions stable: the starting environment, available tools, permissions, task definition, grading criteria, and execution budget. You can intentionally vary these later to test robustness, but first you need a baseline that lets you distinguish normal agent variability from changes in the evaluation setup.

There is no single correct number of repetitions. The amount of evidence you need depends on how variable performance is, how common the task is, how severe a failure could be, and how confident you need to be before making a decision.

A low-risk internal drafting workflow may justify fewer trials. An agent that can modify financial records or take actions on behalf of customers should face a substantially higher bar.

The goal isn’t to produce a large number for its own sake. It is to understand whether a successful run is typical behavior or whether you happened to observe the agent on a good day.

9. Evaluate the outcome, the trajectory, and the tool use

A final answer can tell you that something went wrong. The trajectory often tells you why.

Start with the outcome. Did the agent actually complete the task? Is the resulting system state correct? Were all required deliverables produced?

Then inspect how it got there.

A useful trajectory review asks whether the agent gathered the right information, followed an acceptable plan, recovered when a tool failed, avoided unnecessary loops, and stopped when the task was complete.

Tool use deserves its own attention. Check whether the agent selected the correct tools, supplied valid arguments, interpreted responses correctly, and avoided actions it was not authorized to take.

Finally, look at the resources required to achieve success: latency, number of steps, tool calls, token use, and cost.

These layers help distinguish very different kinds of performance. One agent may reach the wrong answer because it misunderstood the task. Another may understand the task perfectly but fail because it selected the wrong API. A third may complete the work correctly but take three times as many steps and twice the cost.

A single task-success score hides those differences. A useful evaluation makes them visible.

10. Study the failures, not just the average

An overall success rate is useful for orientation, but it rarely tells you what to fix.

Suppose an agent succeeds in 82% of trials. The most useful question is not simply whether 82% is “good.” It is what happened in the other 18%.

Build a failure taxonomy that matches the workflow. Common categories might include planning or retrieval failures, incorrect evidence, reasoning errors, wrong tool selection, invalid tool arguments, permission failures, incomplete execution, recovery failures, excessive looping, unsupported conclusions, or unsafe actions.

Then segment the results.

Look at performance by task type, difficulty, workflow length, tool, environment, and failure severity. Patterns that disappear inside an overall average often become obvious at this level. An agent might perform well on short processes and degrade sharply as the number of steps increases. Another might handle difficult analysis well until an external tool returns an unexpected response.

This is where evaluation starts becoming useful for development rather than merely producing a score. A good evaluation should tell you not only how often the agent fails, but where, how, and under what conditions.

11. Test what happens when the workflow stops being clean

Baseline tests show whether an agent can operate when the workflow behaves as expected. Real systems rarely stay that cooperative.

Once baseline performance is understood, deliberately introduce conditions that challenge the agent's assumptions. Remove information. Return malformed tool responses. Make an API unavailable. Introduce conflicting evidence. Test prompt injection, excessive permissions, out-of-scope requests, and partial system failures.

The goal is not to make the agent complete every task at all costs.

Sometimes the correct behavior is to stop. Sometimes it should ask for clarification, refuse an action, retry a tool, or escalate the case to a human. An agent that recognizes when it should not continue can be safer and more useful than one optimized simply to maximize completion rate.

Stress testing therefore asks a different question from ordinary task evaluation: When conditions deteriorate, does the agent fail in an acceptable way?

For agents operating in consequential workflows, that distinction can matter as much as baseline accuracy.

12. Confirm the results in a limited real-world pilot

Strong offline results are evidence of readiness rather than proof of it.

A controlled pilot tests whether the behavior observed in the evaluation environment survives contact with real users, real data, and real workflow variation. Start with the strongest configuration and limit the potential consequences while you learn.

That can mean restricting the number of users, narrowing permissions, requiring human approval for consequential actions, establishing clear escalation paths, maintaining rollback procedures, and logging incidents in enough detail to investigate what happened.

The important comparison is not just whether the pilot “went well.” Compare pilot performance with the offline evaluation.

If success rates fall, new failure modes appear, or users interact with the agent in ways the evaluation never anticipated, that is valuable evidence. It may indicate that the test environment was too clean, the task distribution was not representative, or an important dependency was missing.

The pilot is therefore part of the assessment rather than the step that happens after it.

13. Turn every meaningful failure into a future test

Agent evaluation should become more useful over time.

When a new failure appears in testing or production, don’t treat it only as an incident to fix. Capture the conditions that produced it, determine whether the problem came from the model, prompt, tools, permissions, environment, or workflow, and turn the case into a reusable regression test.

The basic loop is straightforward:

1. Record the failure and reproduce it.
2. Identify the underlying cause.
3. Make the relevant change.
4. Add the case to the evaluation suite.
5. Rerun the regression tests.
6. Confirm that the fix resolves the problem without introducing another one.

Re-evaluate whenever meaningful parts of the agent system change, including the model, prompts, tools, APIs, permissions, workflow requirements, or execution budgets.

Over time, the evaluation suite becomes more than a benchmark. It becomes a record of what the team has learned about the agent: where it is dependable, where it remains fragile, which failures matter most, and what evidence is required before expanding its responsibilities.

AI agent evaluation example

Let’s take a look at how the pieces of an agent evaluation fit together with an example. Consider a team evaluating an AI agent that reviews vendor contracts before they reach an attorney.

The goal is not simply to see whether the agent can summarize a contract. The test needs to determine whether it can reliably identify important issues, use the right reference material, follow escalation rules, and stay within the limits of what it is allowed to do.

Start with the workflow

The agent is expected to review incoming vendor agreements, compare relevant clauses with approved standards, cite the language supporting its findings, and flag issues that require attorney review.

Its authority is deliberately limited. The agent can analyze and recommend, but it can’t approve agreements or modify the underlying contract.

That distinction matters because it shapes the entire assessment. A useful test must measure not only whether the agent identifies the right legal issues, but also whether it recognizes when a decision belongs to a human reviewer.

Build the test set around real contract variation

The team then creates an evaluation set that reflects the kinds of agreements the agent is likely to encounter.

That might include:

• Standard vendor contracts
• Agreements with unusual payment terms
• Missing required clauses
• Conflicting provisions
• Non-standard liability language
• Contracts that require mandatory escalation
• Difficult examples drawn from previous legal reviews

The purpose is not to create the hardest possible benchmark. It is to reproduce the mix of routine, ambiguous, and high-risk situations that determine whether the agent will be useful in practice.

Cases from previous reviews are especially valuable because they represent problems the legal team already knows can require judgment.

Recreate the environment the agent will actually use

The agent is then placed in a controlled contract-review environment with access to the materials it would need to perform the work:

• The contract under review
• The approved clause library
• Internal review guidelines
• A controlled contract-review workspace

The environment records which documents the agent opens, which tools it calls, and whether it attempts to make any external changes.

Recording the agent’s actions makes it possible to evaluate the agent's trajectory as well as its final answer. If it reaches the correct conclusion using an unapproved source, skips required guidance, or attempts to modify the contract, the evaluation can capture that behavior.

Define what a successful review requires

Before running the tests, the team translates the workflow into explicit evaluation criteria.

A successful trial requires the agent to:

• Identify all issues that must be surfaced
• Support its findings with the correct contract language
• Use approved references
• Follow required escalation rules
• Avoid unauthorized modifications

The last requirement is treated differently from an ordinary scoring criterion. Any unauthorized contract change is a hard failure, regardless of how accurate the rest of the review may be.

This prevents a high average score from hiding behavior that would make the agent unsafe to deploy.

Match each requirement to the right grader

Different parts of the review call for different types of evaluation.

Programmatic checks can verify whether citations point to the correct clauses, whether required outputs were created, and whether the agent attempted to change the underlying contract. A model-based grader can assess more qualitative requirements such as completeness and clarity.

Attorneys then review a representative sample, along with high-risk or ambiguous cases that require professional judgment.

Before the model-based grader is used across the full evaluation set, its judgments are compared with attorney ratings. If the two disagree systematically, the team can refine the rubric or grading prompt before trusting the automated score.

The goal is not to automate every part of grading. It is to automate what can be checked reliably while preserving expert review where the standard depends on legal judgment.

Run enough trials to understand reliability

The same contracts are tested multiple times under consistent permissions, tool access, execution budgets, and starting conditions.

That allows the team to see whether a successful review is repeatable rather than the result of one favorable run.

Across the trials, the team might track:

• Overall task success
• Issue-detection accuracy
• Citation accuracy
• Tool-use accuracy
• Repeatability
• Escalation failures
• Unauthorized actions
• Latency
• Cost per successful review

Looking at these metrics together provides a much clearer picture than a single accuracy score. An agent may identify clauses well but fail to escalate consistently, or produce excellent reviews at a cost or latency that makes the workflow impractical.

Use the failure pattern to make the deployment decision

Suppose the agent successfully completes 89% of the evaluation trials. On the surface, that may look strong enough to justify a pilot.

But the segmented results reveal something more important: in 3% of high-risk cases, the agent fails to trigger a mandatory attorney escalation.

That changes the interpretation of the 89% score.

The question is no longer, “Is 89% good enough?” The relevant question is whether the observed failures violate a requirement the legal team has already defined as non-negotiable.

In this case, they do.

Rather than approving the agent for broader deployment, the team would investigate why those escalations were missed, update the agent or workflow, add the failed cases to the regression suite, and run the test again.

That is the value of a well-designed agent evaluation. It doesn’t simply produce a score. It shows whether the agent is reliable enough for a specific process, where the remaining risk sits, and what needs to improve before its responsibilities can expand.

The same structure can be adapted to other professional workflows:

These are starting points rather than universal standards. The tasks, environment, rubric, controls, and expert reviewers should reflect the specific workflow being evaluated.

How to use AI agent evaluation results

An evaluation is only useful if it changes a decision. The objective is not to produce a universal ranking of agents. It is to determine which configuration, if any, has enough evidence behind it to perform a particular workflow under a defined set of conditions.

Compare agents on the dimensions that matter to the workflow

Start by answering several questions across the following metrics:

• Capability: Can the agent complete the work?
• Reliability: Does that capability hold across repeated runs and different task conditions?
• Operational fit: Can the agent work with the tools, data, permissions, and systems the work requires?
• Risk: Does it stay within the required safety, security, privacy, and compliance limits?
• Efficiency: Are its speed and cost reasonable?
• Evidence quality: Are the test results representative and trustworthy?

The strongest option is the one that meets the overall requirements of your workflow, not necessarily the agent with the highest average task success rate.

Look beyond one overall score

Break the results down by task type, difficulty, tools, workflow length, and failure severity. For example, two agents may both achieve an 85% success rate, but one may fail mainly on low-value edge cases while the other occasionally makes unauthorized changes. Those results should lead to very different deployment decisions.

Set clear deployment gates

Once the results are understood, translate them into a decision. Depending on the evidence, an agent might be:

• Rejected because it cannot meet a core workflow requirement.
• Revised because the failures appear addressable.
• Retested after the configuration or process changes.
• Moved to a limited pilot after meeting offline evaluation requirements.
• Deployed with monitoring after both performance and risk criteria have been satisfied.

Some criteria should remain non-negotiable. If the workflow does not permit privacy violations, unauthorized actions, or missed critical escalations, strong performance elsewhere should not compensate for them.

This is why evaluation thresholds are most useful when they are set before teams become invested in deploying a particular agent.

Use public benchmarks as supporting evidence

Public benchmarks can provide useful outside evidence. They can help teams identify promising models or agents, understand broad strengths and weaknesses, compare model versions, and see how systems perform on relevant task categories.

But public benchmarks cannot reproduce your organization's exact data, tools, permissions, workflows, or controls. Use them as a starting point, then test the exact agent configuration against the work it will actually perform.

For example, WebArena, which tests agents on realistic, interactive websites, found a large gap between a GPT-4-based agent and human performance in its initial study. This illustrates how realistic environments can expose failures that simpler tests may miss.

Mercor's APEX benchmarks can similarly provide external evidence about model and agent performance on professional work. That evidence becomes most useful when combined with private evaluation against the workflow an organization actually intends to deploy.

How Mercor evaluates AI agents

Mercor evaluates the complete agent system instead of just the underlying model. The evaluation process considers whether an agent can successfully complete representative professional work using the required tools, files, context, permissions, and limits.

Realistic workflow evaluation

Mercor tests representative, multistep professional tasks in environments that include the required applications, files, data, permissions, and context. The evaluation looks at both the final output and the steps the agent takes to produce it. This includes analyzing intermediate actions, tool use, changes to external systems, error recovery, and whether the agent follows the required limits.

The test environment doesn’t need to recreate an organization’s entire production system. Instead, it just needs to capture enough of the real-world workflow to reveal problems such as missing permissions, unsafe actions, inappropriate shortcuts, and other issues that could affect performance.

Expert and automated quality assessment

Mercor combines expert-created rubrics, reference evidence, and clear failure criteria with automated checks, model-based evaluators, and expert review. Its framework for evaluating agent trajectories and outputs allows reviewers and automated systems to examine the agent’s actions, final outputs, changes to external systems, or a combination of these factors.

Experts set the evaluation standards, resolve uncertain cases, and validate automated scoring against professional judgment. While model-based evaluation can support the process, it shouldn't be treated as sole authority.

Evaluation-driven improvement

A useful evaluation should make weaknesses diagnosable. Recurring problems may point to the underlying model, prompts, tool definitions, permissions, execution limits, or the workflow around the agent.

Teams can then use those failures to refine the system, add regression cases, and test the updated configuration again before expanding its responsibilities.

The score is therefore only one output of the evaluation. The more durable value is the evidence it creates about where an agent works, where it fails, and what needs to change before it can be trusted with more consequential work.

Building that evidence becomes harder as workflows grow more complex. Evaluations may need realistic environments, domain-specific tasks, expert-defined rubrics, reliable graders, and repeated testing across multiple agent configurations. At that point, the challenge is not simply deciding what to measure. It is operationalizing the evaluation rigorously enough that the results can support a real deployment decision.