Out-of-the-box (OOTB) LLM evaluators promise a shortcut to peak performance. Plug them in, and suddenly you can measure everything from hallucinations to empathy.
It’s an appealing story, especially for teams under pressure to deploy AI agents quickly while maintaining enterprise-grade performance. But the reality is far less convenient. These predefined evaluators often fail where it matters most: understanding the nuance, context, and domain specificity that define real customer experience.
In enterprise environments, that gap between surface-level metrics and real-world performance can mean the difference between a compliant, trusted AI agent and one that quietly fails your customers.
In our previous posts in this series, we explored how AI agents demand new testing frameworks and how sequencing the right testing methods can accelerate quality without slowing release cycles.
Now, we turn to the next challenge: evaluation. Specifically, why enterprises can’t rely on OOTB evaluators to measure what actually matters.
The illusion of objectivity
At first glance, OOTB evaluators seem like a breakthrough. They’re marketed as predefined tools that can detect hallucinations, rate helpfulness, or score conversation quality. Many even cite impressive benchmarks and “alignment with human judgments.”
But the idea of a general-purpose evaluator exposes a deeper flaw: if a universal hallucination evaluator truly worked, leading AI labs like OpenAI or Anthropic would already have solved hallucinations entirely. That paradox reveals the limitation: generic evaluators can’t be universally reliable because the challenges they measure remain unsolved.
The problem isn’t that these evaluators are bad; it’s that they’re generic. Human conversation, especially in a contact center or service environment, is inherently variable. “Empathy,” “clarity,” and “resolution” mean different things depending on the business, industry, and even customer type. What feels empathetic in a debt collection call may sound inappropriate in a medical scheduling scenario.
Yet OOTB evaluators treat them the same, missing the nuance that defines real-world performance.
In practice, that gap between promise and performance shows up in three recurring ways:
- Subjectivity disguised as science.
OOTB evaluators are trained on broad, domain-agnostic datasets. Their judgments often depend on unstated assumptions: what tone is “empathetic,” what step counts as “complete,” what phrasing sounds “professional”.
The result: scores that look precise, but mask inconsistent definitions. - One-size-fits-none domain mismatch.
Customer experience standards are inherently highly variable and differ dramatically across industries. Even within a single company, the criteria for a good conversation can shift depending on the product line, compliance requirements, or geography. Evaluating “conversation quality” isn’t one metric; it’s often dozens of overlapping, domain-specific criteria. For instance, a bank’s definition of “resolution” might require mandatory disclosures, while a healthcare provider’s definition centers on privacy and patient clarity. How could one evaluator, trained on general data, accurately capture both? - False confidence from high scores.
A strong score on a generic metric doesn’t mean your agent performed correctly or safely. It might simply mean the evaluator liked the phrasing, not that the AI agent executed the correct business logic.
Enterprises that equate high evaluation scores on OOTB metrics with readiness risk deploying agents that appear compliant but fail in subtle, costly ways.
Alignment as the foundation for accuracy
Long before the rise in popularity of large language models (LLMs) like ChatGPT, Cresta was training custom NLP models for enterprise contact centers. We learned early that effective models require strong alignment, meaning they depend on agreement between humans about what “good” looks like.
At Cresta, we’ve found two principles are essential for building evaluators that actually reflect human judgment:
1. Unambiguous guidelines
A clear, well-defined guideline is the foundation for an effective model. Guidelines describe the specific criteria the model uses to decide an outcome. This might sound straightforward, but in reality, getting multiple humans to agree on a single guideline – even for a seemingly simple concept – can be surprisingly challenging.
Consider the behavior, “Agent Assumes Payment.” In training custom models, we’d provide dozens of examples of agents exhibiting this behavior and ask different stakeholders to label each as a positive or negative example of the behavior.
For example:
- In a collections context, an AI agent might say, “I’ll go ahead and process your payment now.” Some stakeholders might see this as proactive and confident, while others could view it as pushy or noncompliant because it assumes the customer’s intent.
- In a sales scenario, the same phrasing - “Once you complete your payment, I’ll send your welcome email right away.” - sounds natural and forward-moving, signaling confidence the deal will close.
Both examples describe the same behavior, but whether it’s “good” or “bad” depends entirely on the context. Even among business experts familiar with the space, judgments often differ. Each misalignment creates an opportunity to refine the guideline, clarifying what “Agent Assumes Payment” should mean in a given context.
Because language is understood subjectively by each individual, this exercise is often less about finding the exact “right” guideline and more about reaching consensus, defining how evaluators interpret a behavior consistently. Finding that shared understanding is what establishes alignment.
For models designed to work across use cases and industries, achieving multiple-annotator-alignment–where multiple experts agree on the same judgment–is a key requirement. But in some cases, aligning a model with the preferences of a single expert may be sufficient, creating what’s known as a preference-aligned model. These models are easier to define, since one person’s judgment defines the guideline, but they also have a more specific scope. What works for one domain or stakeholder may not generalize to others.
2. Binary over numeric classifiers
Reaching agreement among multiple humans on a single guideline is hard enough. Now imagine asking them not just to classify an example as yes or no, but to also agree on whether an example deserves a 3 or a 4 on a 10-point scale. The task would be exponentially more difficult, requiring many more guidelines (one for each decision boundary) and many more iterations on each guideline to get to full agreement.
Numerical scales are inherently more subjective than binary classifiers. Even a single reviewer might give different numerical labels for the same example on different occasions.
While this variability is mitigated by binary classifications, it’s magnified when there are more choices and more room for interpretation of each choice.
LLMs amplify this inconsistency even further. Training an LLM judge to make consistent, reliable numerical classification is nearly impossible. Others in the industry have also come to this conclusion, recently noted in Google’s research, A scalable framework for evaluating health language models:
“We first used an iterative process to transform rubric criteria characterized by high-complexity response options (e.g., open-ended text or multi-point Likert scales) into a more granular set of rubric criteria employing binary response options (i.e., boolean "Yes" or "No") — an approach we call Precise Boolean rubrics. The primary objective in developing the Precise Boolean rubrics was to enhance inter-rater reliability in annotation tasks and to generate a more robust and actionable evaluation signal, thereby facilitating programmatic interpretation and response refinement. The increased granularity afforded by the simple Yes/No format mitigates subjective interpretation and fosters more consistent evaluations, even with a larger number of total questions.”
This mirrors findings from our own research on aligning LLM judges for hallucination detection in contact center conversations.
Learn more in our paper FECT: Factuality Evaluation of Interpretive AI-Generated Claims in Contact Center Conversation Transcripts, accepted at KDD 2025, which details our methodology and the supporting benchmark dataset we open-sourced.
Trustworthy evaluators require a human-in-the-loop system. The only sustainable way to achieve scalable, fully-aligned LLM judges is through continuous human calibration. By injecting human preferences, reviewing outputs, and iteratively refining the system, teams can ensure LLM judgments align with expert expectations as reliably as expert annotators themselves (and without the labeling fatigue!).
Cresta’s evaluators: Expert-aligned, transparent, and proven
We started this post by saying that you shouldn’t trust out-of-the-box evaluators from vendors, so it’s natural to ask what makes ours different. The difference lies in domain expertise, transparency, and alignment rigor.
Cresta’s library of evaluators and human-centric systems are optimized specifically on the contact center domain and proven to generalize across a wide range of customer service use cases. Each evaluator is aligned with multiple human experts to ensure high inter-annotator agreement (as measured by metrics such as Kappa statistics). Unlike opaque vendor tools, Cresta’s guidelines are exposed to customers, so you can understand exactly what each LLM judge is doing, and even adjust the criteria to better fit your organizations’ standards. This results in evaluations you can trace, trust, and tailor.
Evaluation as an engine of trust
Ultimately, evaluation isn’t just a technical challenge; it’s a trust challenge. OOTB evaluators make big promises about objectivity and scale, but in practice, they deliver metrics detached from meaning. Trustworthy evaluation is about aligning models with human judgment, business context, and real-world standards.
As AI agents become integral to customer experience, evaluation itself becomes a continuous discipline, one that must evolve alongside your system, data, and business.
Enterprises that ground their evaluators in expert alignment, transparency, and domain expertise can move faster with confidence, building not just better testing pipelines, but more trustworthy AI agents.
Our next post in this series will explore how Cresta built its evaluator system, from preference alignment to production readiness, and how that foundation enables rapid, reliable AI Agent development.
