Table of contents
Highlights
- Agent evaluation measures how well AI agents complete multi-step tasks, going beyond single-output accuracy to assess reasoning, tool use, and safety.
- Enterprise agents require evaluation across six dimensions: task completion, reasoning quality, tool-use effectiveness, safety, escalation behavior, and business impact.
- Most existing evaluation approaches target developers, leaving enterprise IT and HR leaders without production-focused guidance for their specific use cases.
- Common agent failure modes in production include hallucinated tool calls, infinite loops, stale memory, and dead-end reasoning that traditional testing may not catch.
- Moveworks embeds evaluation and production oversight directly into its platform, helping enterprise teams monitor agent performance across IT, HR, finance, and operations.
Enterprise teams deploying AI agents face a critical gap: standard developer benchmarks don't measure what IT and HR leaders actually need to succeed in production.
For developers building AI agents, the focus is often on technical benchmarks, like response accuracy. While critical to agent success, these scores don’t reveal what IT and HR leaders need to understand whether agent deployment is succeeding in real-world production.
For example, developers may want to know if an agent passes a certain benchmark. But if you’re an enterprise leader responsible for making sure agents reliably serve thousands of employees daily for self-service automation, you need a broader view of performance.
Specifically, you need insights into reliability (does the agent consistently complete tasks?), safety (does it stay within its permissions?), and other business impact metrics that help you measure AI ROI.
What is agent evaluation?
Agent evaluation measures how well AI agents can complete realistic, multi-step tasks across diverse employee requests.
This goes deeper than simply evaluating whether or not an agent returns an accurate answer. To understand its ability to support employees in production and contribute to ROI, you have to evaluate whether it can reason and correctly call and use tools to resolve requests end to end — reliably and safely.
There are three main grader types for agents: code-based (deterministic checks), model-based (AI judges), and human (domain experts review agent behavior).
Grader Type | Description | Pros | Cons |
Code-based | Deterministic checks evaluate whether or not an agent achieved the expected outcome. | Fast and cheap | Lacks nuance |
Model-based | AI judges apply rubric-based scoring to evaluate agent behavior. | Flexible and scalable | More expensive than code |
Human | Domain experts review agent behavior. | Superior quality | Slow and expensive |
Why enterprise agent evaluation is different
While AI agent evaluation assesses whether an agent can perform tasks, enterprise agent evaluation widens the scope to consider how it operates within a business environment.
Take a typical enterprise-scale organization where agents and employees operate daily across dozens of mission-critical systems, like ServiceNow, Workday, Okta, Slack, and Teams. Rather than just testing whether specific tasks can be executed, enterprise agent evaluation must also account for the compliance and data privacy requirements that come with accessing sensitive enterprise systems, as well as the complexity of coordinating agentic workflows across multiple departments.
Many evaluation frameworks stop short of this broader enterprise focus. They were designed for the teams building AI agents that need to know technical measures, like response time — but that leaves enterprise IT and HR leaders, who need eyes on reliability, employee experience, and business outcomes, in the dark.
6 dimensions of enterprise agent evaluation
For leaders preparing agents for IT and HR workflows, there are six key dimensions to evaluate to understand whether agents are ready for deployment — but standard benchmarks often miss them entirely.
Use this framework as a practical checklist to evaluate your agents for production deployment and ongoing performance monitoring:
Task completion accuracy
You need to know whether agents can fulfill requests to help IT and HR teams get work done. Though it may seem like a straightforward evaluation, testing for task completion can be tricky.
Because an agent has a 90% answer rate doesn’t necessarily mean that 90% of employee requests are getting completed. An agent can return a simple response to an employee’s query without taking the actions required for task resolution.
That’s why an evaluation framework must assess whether or not an agent can resolve employees’ requests from start to finish, not simply deflect their request to another team or system.
Reasoning quality
Agents that can’t reason have no reason to be deployed in your enterprise. When an agent can’t interpret requests and determine next steps, it ends up driving unnecessary escalations, ultimately pushing more work onto employees' plates, aggravating teams, and wasting everyone’s time.
You need to evaluate how well agents can understand complex requests and break them down into logical, sequential steps to achieve end-to-end resolution. By assessing an agent’s trajectory evaluation, you can understand whether it consistently takes an efficient path to resolution or gets stuck in loops, misses obvious steps, or has to backtrack to correct mistakes.
For example, if an employee asks for payroll access, can the agent determine whether the issue is a forgotten password or a permissions problem? Check the employee’s account and access authorization? Grant access or route for approval? Or does it repeatedly ask for information it already has?
Tool-use effectiveness
Calling the wrong tool or giving it the wrong inputs comprises one of the top six major categories of agent failure, and those tool call failures can be hard to spot. According to a study on agent evaluation methods, “aggregate benchmark scores often hide where tool use fails.” In other words, a high overall score may mask certain mistakes.
To help mitigate tool call failures, you need an evaluation framework that assesses whether agents can select the correct tools, correctly format inputs, and properly interpret tool responses.
Safety and guardrail adherence
Research from NIST reveals AI agents can find ways to cheat traditional evaluations so it looks like they’ve completed a task when they haven’t.
There are two ways AI agents dupe evaluations:
- Solution contamination: The agent accesses information that shows it how to complete the evaluation task, effectively finding a cheat code.
- Grader gaming: The agent uses a gap, misspecification, or other weakness in the evaluation’s scoring system to figure out a way to score well without completing the task.
These findings show how agents can exploit weaknesses in traditional evaluations and underscore the importance of using a production-focused evaluation framework that goes beyond technical benchmarks. Specifically, you should evaluate whether agents:
- Respect data access controls
- Avoid hallucinated actions
- Follow compliance policies
- Avoid unauthorized access
Escalation behavior
Not every request can (or should) be resolved by agents. Especially for sensitive matters in HR, like leave requests, agents need to hand off certain tasks to human employees for approval — but knowing when and how to orchestrate those hand-offs is, in itself, a critical evaluation dimension that’s often overlooked.
Here are two examples of correct escalation behavior:
Department | Employee request | Why the agent escalates | How it hands off |
IT | An employee asks an agent to grant them VPN access. | The agent realizes the request requires an exception to the standard policy — and it doesn’t have the authority to approve it. | It hands off the task to an IT employee, delivering the full context: the employee’s request, what it tried to complete, and why the request requires human intervention. |
HR | An employee asks an agent what a new dependent means for their benefits eligibility. | The agent identifies the request involves an edge case and can’t confidently respond. | It escalates the query to an HR specialist, including full context so the employee doesn’t have to repeat their question. |
Without ensuring agents know how to properly escalate tasks, your organization may risk productivity declines or compliance complications.
If agents unnecessarily escalate routine requests to human teams, then IT and HR staff can quickly become inundated with tickets, adding more work (and frustration) instead of providing relief. But if agents avoid escalations and take action outside their permissions, they may inadvertently open the door for potential compliance violations.
Business impact
A recent workshop paper named reliability, or “consistent correct behavior over time,” as the top development challenge.
Without evaluation frameworks that go beyond technical metrics to effectively determine whether agents resolve employee requests and help improve operational performance, enterprise leaders don’t have a way to measure how agents contribute to real-world business impact and ROI.
One way to measure business outcomes is to connect agent performance to operational metrics, such as MTTR reduction, ticket deflection rate, hours saved, and cost per resolution. This way, instead of just focusing on accuracy scores, you can better understand how agent deployment impacts ROI.
How to build an enterprise evaluation framework
Start with capability evaluations to test what your agent can do initially. Because these are new capabilities, it’s normal for pass rates to be low. Actually, this is the point. This way, your team can identify gaps and create goals for improvement.
Next, you can move on to regression testing, where you re-evaluate test cases the agent has already mastered. Here, you should expect near-100% pass rates.
An effective enterprise evaluation framework evaluates agents before and after deployment:
- Pre-deployment: Build evaluations alongside agent development to identify capability gaps early and guide priorities.
- Post-deployment: Continue evaluations for ongoing production monitoring to detect performance drift and catch new errors.
Besides helping you monitor business impact and ROI measurement, ongoing agent evaluation is critical for safe deployment, particularly when it comes to authentication, authorization, and governance. In fact, in early 2026, NIST’s Center for AI Standards and Innovation (CAISI) published a formal Request for Information on AI agent security, calling for input to help craft federal standards for autonomous AI systems.
Common agent failure modes in production
Once in production, even the most thoroughly tested agents will still fail. The most common failure modes include hallucinated tool calls, over/underuse of tools, infinite loops, stale memory, or dead-end reasoning.
Often, these failures are the result of small errors that compound across multiple steps, eventually upsetting the entire workflow, such as:
- Interaction sequences: An early mistake leads to more downstream errors.
- State changes: Outdated or incorrect information upsets the agent’s response.
- Tool dependencies: If a tool call fails or is otherwise used incorrectly, it can disturb the entire task.
Unlike traditional model errors, which usually only affect one agent answer, these production failure patterns ripple across multiple steps. Agent evaluation should consider multi-step dynamics (e.g., planning and tool use) so one early mistake doesn’t upend the entire workflow.
To help catch mistakes before they spiral and prevent new ones from developing, map observed failures to known failure classes — like tool-user error or stale memory issues — and incorporate them into future evaluations.
Make agent evaluation a competitive advantage
Agent evaluation is an important technical exercise for developers to test and improve agent performance. But it can also become a strategic advantage for enterprise teams to maximize business impact.
By using rigorous evaluation processes to identify issues before they can impact employees and operational metrics, organizations can deploy agents more confidently and with fewer risks.
Moveworks Reasoning Engine is designed to provide built-in evaluation and monitoring for enterprise agents, extending beyond standard benchmarks to capture task completion, reasoning quality, tool-use effectiveness, and business impact. Meanwhile, Employee Experience Insights helps leaders prioritize projects to continuously improve agent performance.
As AI adoption continues to accelerate, agent evaluation and monitoring can become key differentiators that support faster deployment, lower risk, and stronger business impact.
See how Moveworks helps enterprises evaluate and improve AI agent performance across IT, HR, and beyond.
Frequently Asked Questions
Agent evaluation is the practice of measuring how well AI agents perform tasks in realistic, multi-step scenarios. It goes beyond traditional model testing to assess whether agents can reliably complete goals end-to-end, including how they reason, use tools, and handle errors along the way.
LLM evaluation typically scores a single output against a prompt, while agent evaluation examines multi-step workflows where reasoning, tool use, and feedback loops all interact. Because agents can follow different valid paths to a solution, evaluation must assess trajectory quality and outcome stability across multiple runs.
Key metrics include task completion rate, reasoning quality, tool-call accuracy, safety and compliance scores, escalation appropriateness, and business impact measures like mean time to resolution and ticket deflection rate. Tracking both technical and business metrics gives enterprise teams a complete picture of agent performance.
Production evaluation combines automated monitoring with periodic human review. Teams typically start with capability evaluations to identify what agents handle well, then graduate high-performing evaluations into regression suites that run continuously to catch any drift or degradation over time.
Escalation behavior determines when an agent hands off to a human support agent and how much context it provides during the handoff. In enterprise environments, poorly timed or context-poor escalations can frustrate employees and slow resolution times, making this a critical evaluation dimension.
