What Is Eval Engineering?

In November 2022, a man named Jake Moffatt visited the Air Canada website to book a flight to his grandmother's funeral. He asked the airline's chatbot about bereavement fares. The chatbot told him that he could book a full-price ticket now and apply for a bereavement discount within 90 days. The chatbot was wrong. When the airline refused the refund, they argued the chatbot was a separate legal entity responsible for its own actions. A tribunal ruled against them, stating that a company is responsible for all information on its website, regardless of the source. Air Canada owed Moffatt $812.

This incident crystallized a fear that every executive deploying AI already feels:

What happens when the system says something wrong, and nobody catches it until it is too late?

Air Canada had the right policy written down, but they lacked a system to ensure their AI actually reflected that policy. The teams that are shipping production AI are using the same LLMs as everyone else. The difference is what happens after generation. The evaluation layer. The part the industry treats as an afterthought is actually the whole game.

Defining eval engineering

Eval engineering is the discipline of building production-grade evaluation systems that govern AI behavior at scale. It treats evaluation not as a checkpoint but as infrastructure. It is the layer where your domain expertise becomes executable — where your definition of "good" becomes enforceable at every interaction, not just the ones you sample.

What is an eval?

To clarify the terminology: an "eval" is short for evaluation. An AI evaluation is an automated test that compares AI outputs against defined success criteria. Eval engineering is the broader discipline of building the systems that manage these evaluations.

Traditional evals vs. eval engineering

Most teams think about evaluation as a quality check, something you do before shipping. A gate to pass through on the way to production.

• Traditional evaluation asks: "Did this work?"
• Eval engineering asks a harder question: "How do we ensure this keeps working, at scale, in production, as conditions change, while catching failures before users ever see them?"

The first question gets you a report. The second gets you a system.

This distinction matters because measurement alone changes nothing. Evals tell you that 30% of outputs were bad, but they don't stop those outputs from reaching users. They don't improve over time. Eval engineering inverts this entirely, transforming measurement into action: if an output fails evaluation, it doesn't ship. If patterns of failure emerge, the system adapts. If domain expertise exists in someone's head, it gets encoded into infrastructure that runs at scale.

The shift in mental model looks like this:

Eval engineering is not...

To understand what this discipline is, it helps to define what it is not:

• Eval engineering is not testing. Testing asks whether something works. Eval engineering ensures it keeps working.
• Eval engineering is not just observability or metrics. While these provide the essential visibility to diagnose system state and performance, eval engineering acts as the proactive governance layer that enforces quality standards before they reach the user.
• Eval engineering is not a project. Projects end. Eval engineering is a capability you build and maintain indefinitely because production data drifts and user behavior shifts. Plus, model providers may push updates without warning.
• Eval engineering is not traditional ML. ML evals test model accuracy on held-out datasets. Eval engineering operates at the application layer, evaluating system behavior in production with domain-specific criteria at 100% coverage. The unit shifts from "model" to "deployed application," and the timeline shifts from "before launch" to "forever."
• Eval engineering is not context engineering. You can assemble the perfect context and still produce sub-par outputs. Context engineering determines what goes in. Eval engineering determines whether what comes out is any good. They're two halves of the same system. The engineers who master both will define the next generation of AI development.

The state of AI development: Challenges and opportunities

Modern LLMs and agent frameworks have compressed development from months to days. Spinning up a RAG system takes an afternoon. Getting an agent to call tools takes a few hours of prompt engineering. The building part is largely solved. And yet most AI projects never make it to production. The teams that are shipping production AI are using the same LLMs as everyone else. The difference is the evaluation layer — the part often treated as an afterthought is actually the whole game.

Reasons why AI projects stall

Organizations often stall because they lack certainty. No VP of Product is going to greenlight a production deployment they can't explain. No legal team will approve a customer-facing agent without understanding what could go wrong and how often. No CFO will fund the next phase of an AI initiative that can't quantify its own reliability. Organizations don't kill AI projects because the technology failed. They abandon them because nobody could prove it worked.

Evals are the mechanism that creates that proof. They allow product teams to say, with evidence, "this works in 97% of cases, here are the 3% it doesn't, and here's what happens when it fails."

The warning: Common reasons for abandoning AI

Gartner predicts that over 40% of agentic AI projects will be abandoned or cancelled entirely by the end of 2027. The common assumption is that they lack talent, or models, or frameworks. But look closer: they have plenty of prototypes. What they lack is the ability to trust what they've built. This trust gap shows up in predictable patterns:

• The endless pilot loop: Projects that demo well but never graduate to production. Stakeholders ask for "just a bit more testing."
• The firefighting trap: Teams react to production failures instead of preventing them. Each incident erodes confidence.
• The rebuild tax: Every new project reconstructs evaluation infrastructure from scratch. No shared learnings.
• The accuracy plateau: 70% accuracy that never improves. Teams try different prompts, switch models, add more test cases. Nothing moves the needle because they're optimizing within a broken paradigm.

The teams shipping production AI at scale have figured out one thing: you can swap your model in a weekend. You can't swap your understanding of what good looks like. Teams who successfully ship agents to production don't differentiate on the models they call — they differentiate on how they know it's working.

Benefits of eval engineering

The first benefit of evals is specification and clarity. Two engineers reading the same product spec will interpret edge cases differently. “The agent should be helpful" means different things to different people. An eval suite resolves this ambiguity by forcing the team to articulate what success actually looks like. The act of writing test cases exposes gaps in your requirements that would otherwise surface as bugs in production.

Evals also accelerate everything. When a new model rolls out, teams without evals face weeks of manual testing. Teams with evals run their suite overnight and know by morning which capabilities improved, which regressed, and whether the upgrade is worth it. They tune prompts, validate changes, and ship in days while competitors are still guessing.

Evals also become the communication channel between product and engineering. Instead of vague requests to "improve quality," product teams can point to specific failing cases. Over time, your eval suite becomes institutional knowledge: the system remembers what individual engineers forget.

The value of evals compounds over time. Evals require upfront investment in time and infrastructure, but they deliver long-term value by turning subjective opinions into data-driven evidence. Beyond providing performance baselines, evals help with:

• Communicating between product and engineering.
• Preventing unnecessary over-engineering.
• Building a permanent library of institutional knowledge that captures past edge cases and simplifies team onboarding.

Why evals are critical today

Multiple forces are converging to make Eval Engineering an urgent priority:

1. Economics: The model layer is commoditizing fast

GPT, Claude, Gemini, Llama, and more are converging in capability and competing on price. Switching costs are dropping. The model you use matters less every quarter. Differentiation is moving to the application layer: how you deploy, how you customize, how you ensure quality.

The teams that win will be the ones who can reliably ship, not just prototype. Eval Engineering is the capability that enables reliable shipping.

2. Trust has become the bottleneck

The enterprises falling behind in AI adoption are not lacking models or frameworks or talent. They're lacking trust.Their legal teams won't sign off. Their compliance teams have concerns. Their executives got burned by a demo that failed in production and are now hesitant. Eval Engineering is about systematically manufacturing trust. It turns “we think it works” into “we can prove it works, and we can prove it keeps working.”

3. The industry shift: How agents get built

Agent Development Lifecycle (ADLC) is a rethinking of the traditional SDLC for probabilistic systems. The pattern across the ADLC framework from multiple companies is consistent: less time on upfront planning, significantly more time on tuning and optimization, and far greater emphasis on automated governance.

At the heart of every ADLC framework sits evaluation. Eval Engineering is the craft that makes ADLC actually work.

The strategic imperative: The value of evals

In the future, evals will be more valuable than your code. Evaluations encode your definition of quality and capture your domain expertise. Models are commodities you can swap, but your evals are yours. They embody how your organization thinks, what your customers expect, and what "good" means in your specific context. A company's catalog of evals will become core IP. The teams that treat eval development as a strategic investment will have assets their competitors cannot replicate.

FAQs about evals and eval engineering