AI Engineer Europe 2026
The maturity phases of running evals
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The maturity phases of running evals
Agent evaluation grows from documented human judgments into production replay, trace-level checks and careful reconstruction of the systems an agent touches.
From a talk by Phil Hetzel
Why do promising prototypes stall before production?
Building a generative AI proof of concept is one thing; gaining enough confidence to put it in front of real users is another. Phil Hetzel encountered that gap while consulting: his customers were prolific at creating prototypes, but much less successful at bringing them into production. He describes a path through KPMG and Slalom, where he led the global Databricks business unit, before adopting Braintrust and eventually joining it to lead solutions engineering. That experience frames the practical question behind evaluation maturity: what evidence does an increasingly capable agent need before you can trust it with real work?
Evaluation and observability form a continuous confidence loop. In Braintrust’s framing, evals build confidence before deployment; observability maintains it afterward. They address the same agent-quality problem from different points in the lifecycle, and the infrastructure supporting them must evolve as the underlying technology changes.
Quality means that an agent behaves as expected when it encounters real users and real usage. The risks extend beyond an incorrect answer: an unkind or unhelpful response can damage a brand, excessive resource consumption can make the system expensive, and inappropriate behavior can create compliance or legal exposure. Evals also support improvement. For each change to an agent, they provide evidence about whether the application got better and by how much.
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Start with failure modes, then define the evaluation
Begin with the failures that matter. A builder or subject matter expert can identify where an agent goes wrong and construct evaluations around those cases. Hetzel contrasts this with an exhaustive unit-testing mindset: the point is not that unit tests literally cover every possibility, but that enumerating every possible agent failure is an unbounded task. Trying to do so leaves little time to ship.
Evaluation results can be useful without being perfect. An LLM-as-a-judge evaluator may produce nondeterministic judgments, yet still provide directional evidence that a change is helping. That makes the evaluation’s structure especially important: you need to know what ran, which examples exercised it and what the scores actually assessed.
| Primitive | Role |
|---|---|
| Task | The agent or prompt under test |
| Dataset | Examples that invoke the task |
| Scoring functions | Assessments of the task’s utility or quality |
An example starts the workflow; the task produces behavior to inspect; the scoring functions assess that behavior. Keeping these responsibilities separate makes it possible to change the agent while retaining a useful basis for comparison.
The maturity framework follows increasing complexity rather than a set of certification gates. Its four areas are getting started, measuring to manage, accounting for complexity and advanced evaluation techniques. As agents gain capabilities, they gain additional ways to fail, and their evaluations must expand accordingly. These are overlapping areas on a continuum, not rigid stages every team must complete in the same way.
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Getting started: write down why an answer is good
Manual inspection is a reasonable starting point if it leaves a record. Hetzel’s starter example is to run roughly ten inputs through an agent and inspect the outputs. This is an illustrative batch, not a statistically validated sample-size requirement. The reviewer can be the builder, but a subject matter expert is preferable when recognizing a good response requires domain knowledge.
For each response, collect two things:
- A thumbs-up or thumbs-down judgment.
- A written explanation of that judgment.
The explanation is the reusable part of the expert’s knowledge. A negative rating tells you that something failed; its justification tells you what the reviewer noticed and why it mattered. Capturing that reasoning creates material for an automated evaluator later, instead of leaving the quality standard inside the reviewer’s head.
An audience member describes working without evaluation infrastructure: run an agent on their data, inspect the result, create a prompt and repeat. The immediate improvement is to preserve what that loop teaches you. A trace comes in, someone judges it, and the justification becomes part of the record that can inform a future judge.
The review interface matters too. Hetzel shows Braintrust’s human annotation view and describes custom annotation views that users can generate and tailor. A generic trace dump may not match how an expert understands the work. Present traces in a form reviewers recognize so they can evaluate them appropriately and contribute useful feedback.
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Measuring to manage: turn explanations into scorers
Once explanations accumulate, use them to identify recurring failure modes. Hetzel suggests analyzing the reasons behind negative ratings with tools such as Cursor, Claude Code or Codex. The goal is to derive explicit descriptions of what goes wrong, then automate assessments that previously depended on a small number of experts.
An LLM judge can scale assessments that require interpretation, but assigning a model the role of judge does not make its decisions trustworthy. Its outputs also need evaluation. For objective failure modes, ordinary code may be a better fit: an agent that exceeds a tool-call budget or consumes too many tokens can be checked deterministically.
A small TypeScript scorer can express those two budget checks directly. The limits are inputs because the acceptable budget depends on the application:
typescript
type Usage = {
toolCalls: number;
totalTokens: number;
};
type Limits = {
maxToolCalls: number;
maxTotalTokens: number;
};
function scoreUsage(usage: Usage, limits: Limits) {
return {
toolCallBudget: Number(usage.toolCalls <= limits.maxToolCalls),
tokenBudget: Number(usage.totalTokens <= limits.maxTotalTokens),
};
}
Each score is 1 when the corresponding budget is respected and 0 when it is exceeded. Keeping them separate preserves which failure occurred; neither score attempts to decide whether the answer was helpful.
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Use production traces to guide improvements
At this point, the evaluation dataset should include production traces—or at least traces from user acceptance testing. The purpose is to exercise the workloads the agent actually encounters. Think of evaluation as rerunning production: capture real examples so offline experiments can test changes against real usage.
That creates an improvement loop:
- Observe: capture agent traces in production.
- Analyze: use human review or automated tooling to understand failures.
- Evaluate: bring those examples into an offline experiment and rerun them.
- Improve: use the results to guide changes to the agent.
The same production behavior that exposes a weakness becomes evidence for deciding what to change. This connects observability to evaluation and gives the evaluation process a role beyond blocking regressions.
LLM judge scoring extends this loop beyond the throughput of human-only review. The scoring-function example here illustrates the next step after collecting expert judgments: make the assessment repeatable across more examples, while retaining the quality criteria extracted from those judgments.
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Account for complexity across the whole trace
Once an agent interacts with external systems, evaluating only its final response becomes insufficient. Hetzel distinguishes two useful categories of tools:
| Tool category | What it does |
|---|---|
| Context gathering | Retrieves data and supplies it to the LLM |
| CRUD | Creates, reads, updates or deletes external records |
Both can improve an agent’s usefulness, but each external interaction adds opportunities for failure. The distinction directs attention toward what a call contributes to the model’s context and what it does to the surrounding system.
The unit of inspection expands from the output to the entire agent trace. Capture the sequence of steps, inspect how the agent arrived at its result, and target evaluations at individual tool or MCP calls where necessary. A final answer alone cannot expose every problem in the path that produced it; trace-level visibility supplies the intermediate behavior that those checks need.
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Replay external interactions without changing production
External systems create two distinct replay problems. First, an offline evaluation must represent the state those systems were in when the original input arrived. Their current state may be different, and the offline environment may not have access to them at all. Second, replay must not overwrite production data while exercising the agent’s create, update or delete operations. Hetzel describes this as an incompletely solved problem, with approaches that approximate production rather than guarantee a perfect reconstruction.
Mock APIs can provide a place to exercise interactions against a representation of external state. Hetzel then proposes treating the trace as a container for much richer context than conventional application tracing often carries. Store relevant system state alongside the original execution and inject it into the task during evaluation. That can encapsulate part of the replay environment inside the example itself and reduce the separate test infrastructure required. His description of potentially very large traces is a design premise here, not a demonstrated storage-capacity guarantee.
A further option is to query the external system as it existed at the original execution time. For example, a vector database might support timestamp or version queries that recover the retrieval state associated with a captured input. This depends on the database’s capabilities, configuration and retained history; it is not a universal vector-search feature. Where available, it addresses a specific replay mismatch: today’s retrieval results may not be the results the agent could see when the example was recorded.
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Automate discovery, then evaluate the judges
The prepared material closes with two emerging patterns. Topic modeling across production traces could help uncover recurring failure modes automatically. Coding agents such as Claude Code, paired with an evaluation provider’s CLI, could automate more of the evaluation workflow. These are directions for further development in the talk, not demonstrated implementations. The accompanying slide also lists simulated user interactions, without developing that third idea in the spoken explanation.
The closing audience question brings the discussion back to the reliability of scoring: should evaluation platforms favor deterministic graders, or embrace LLM judges? Hetzel’s answer starts with the nature of the criterion. Some aspects of agent behavior are subjective, so an LLM judge can be appropriate. The requirement is to evaluate that judge extensively against what a human would decide in the same circumstances.
A concrete way to do that is to constrain the judge to discrete decisions and create a ground-truth dataset of human judgments. Those labels make the judge’s decisions easier to compare with the intended standard. Discrete output is a property of this chosen rubric, not an inherent property of every LLM judge. Deterministic checks still handle objective conditions; subjective judgments gain credibility through demonstrated alignment with human decisions.
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Resources
From the talk
Create and customize React interfaces for reviewing traces, recording feedback and correcting dataset examples.
Further reading
Choose scoring methods, evaluate individual spans or whole traces, and test evaluators against examples.
Organize production logs, human feedback and curated examples into versioned datasets for repeatable evaluation.
Updates since the talk
Current instructions for running evaluations, querying logs, syncing data and configuring coding-agent integrations.
Classify production traces by task, sentiment and issues, then use recurring patterns to build evaluation datasets and review queues.
Read the complete timestamped transcript
- 0:00
[on-hold electronic music] Welcome, everyone.
- 0:15
Um, it's always a challenge to be a presenter directly after lunch because that's typically when the energy level goes from right around here to around here. But I'm gonna try to make this session worth your while, uh, today.
- 0:27
We've got 18 very quick minutes, uh, together, and, uh, during that time, I'm gonna be talking about the, uh, different maturity levels that I see people go through as they perform evals for their agents.
- 0:41
Uh, before I get into that, um, just roughly quick agenda today. Um, I'll explain a little bit about myself, the company that I work for. We'll spend most of the time today on, on more theoretical concepts, not product concepts.
- 0:54
And then, um, we'll, we'll talk about, um, where I, I think this field is going in the future. Uh, also, make sure to leave enough time, hope- hopefully a couple minutes, for questions as well.
- 1:05
I didn't over-prepare the content in, in hopes that we could have a little bit more of a discussion, uh, at the end of this. Uh, first of all, this is me.
- 1:13
My name is Phil Hetzel. I lead solutions engineering for a company called Braintrust. Effectively, what that means is that it is me and my team's job to make sure that people are getting the most value out of the platform as quickly as possible.
- 1:28
Prior to Braintrust, I spent 12 years in consulting and systems implementation. Uh, first four years with KPMG, last, uh, eight years in consulting with a company called Slalom Consulting.
- 1:40
And with Slalom, I led their global Databricks business unit. And I noticed that a lot of my customers were prolific at creating generative AI proofs of concepts. They were not as prolific at bringing those proofs of concepts to production.
- 1:53
So I started using Braintrust first as a user because I wanted to help bridge that gap, uh, for my customers. And I liked the product so much that I ended up joining the company, and I've, I've been here for about a year.
- 2:07
Um, outside of, of work, I like to play chess, but I'm not very good at it, and I like to spend time with my wife and my dachshund. His name's Pistol Pete.
- 2:16
Um, he's the one in brown, not the one in, in black. [laughs]
- 2:20
Uh, what is Braintrust? Uh, the company that I work for. Braintrust is an agent quality company. Um, one of the, uh, I guess two of the main ways that we contribute to agent quality are evals and observability, which we consider to be very much the same problem, uh, from a systems perspective.
- 2:39
Evals, of course, being the thing that you're doing in order to gain confidence in your agent as you want to bring it to production, and then observability being the practice of once that agent is in production, remaining confident in it.
- 2:55
Um, it's a growing space. It's a very fast-moving space. And, uh, in, uh, when you build an evals platform, you really have to grow with the technology, the underlying technology, as it changes.
- 3:07
So it's a very fun, uh, fun place to be in.
- 3:10
Uh, let me give, like, a, a, a quick overview of the problem. Uh, we talked a little bit about why we do evals in the first place. How many you, of you all are doing evals today, hopefully, as, as you build?
- 3:22
Um, every single hand should be up. And, and certainly, when I give this talk next year at this conference, all you are gonna come back, of course, to this session, and every hand is gonna be up.
- 3:32
Eval is very important. The reason why we do evals is wholly in service to agent quality. That's the most important thing. We wanna make sure that our agents are doing what we expect when confronted with real usage and, and, and real users.
- 3:47
Um, this is really important from a risk perspective and, and a brand perspective. We don't want, um, the reputational risk of an agent being unkind or unhelpful to a customer.
- 3:59
We don't want the systems risk of an agent costing us too much money as it, as it operates. Um, and there could even be compliance and legal risks if your agent goes too far off the rails.
- 4:10
So evals are a de- both a defense against those types of risks, but they're also, uh, they can play offense with evals in knowing with each tweak that you make to your agent how it's improving and how much it's improving your application.
- 4:26
Um, a couple of primitives here. Evals are not unit tests, where- whereas unit tests are very exhaustive in, in how you perform them. With evals, you wanna make sure that you start very high level with the failure modes of your agent.
- 4:41
Either, either you or a subject matter expert can educate about the specific failure modes of an agent, and you build evals around those very specifically. What you don't do, like you would with unit test, is think about exhaustively every single thing that could potentially go wrong with your agent and try to make an eval for it.
- 4:59
Why can't we do that? Because it's, it's infinite. You would spend all of your time writing tests and none of your time shipping, which is, which is not productive.
- 5:07
Uh, eval re- results don't, don't need to be perfect. Sometimes they can be d- sometimes they can be directional. Um, using LLMs to judge o- other LLMs, LLM-as-a-judge techniques, you're probably not gonna get 100% every time.
- 5:20
That's okay. As, as long as you're trending in the right directions with those more non-deterministic techniques, um, that, that, that is completely fine. Um, different primitives with the eval, uh, itself, how it's constructed.
- 5:33
You have three things. You have a task. That's the, uh, agent under test or the prompt under test. You have some dataset of examples that initiate that task. How do you invoke that task?
- 5:46
You use some example that you give to an LLM or give to an agent to, um, to start that workflow. And then you have certain scoring functions which you're using to judge the utility or the quality of that task.
- 5:59
Um, there are a couple of different maturity, uh, areas that I've noticed some of our customers go through. Um, I've listed four here. Th- this is probably more of a continuum than, than being very discrete.
- 6:10
But suffice to say that these stages, um, uh, you know, uh, you will, you will traverse these stages as and when you create more complexity within your agent just by, just by necessity.
- 6:23
Uh, the more complex, uh, agent you're, uh, that you're building, the more vectors there are for failure, the more failure modes you... that you may need to account for.
- 6:33
Um, we're only gonna be focusing on the, uh, like eval theory itself today. We're not g- gonna really talk about the platform surrounding evals. Uh, we've got a booth for that downstairs.
- 6:45
Uh, if, if you're interested, you can come find me. So go through these four: just getting started, uh, measuring to manage, accounting for complexity, and then, um, some advanced eval techniques.
- 6:57
Okay, just getting started. It's not wrong to just get started with, with vibes. I know, like, like vibe checking is a, a very, uh, uh, nasty phrase here at, at this conference.
- 7:07
I actually think it's okay. It's, it's certainly better than nothing. Um, when you're first starting out, you can't help but start with vibes. I think the only thing that I would really recommend is that as you are vibe checking, you're also documenting.
- 7:21
So when you have an agent under test, you give that agent maybe 10 different example inputs and, and, and loop through those inputs to see what the output is.
- 7:32
You should probably have some human, whether it's the person who built the agent or even better, a subject matter expert that really knows what a quality response would look like.
- 7:42
You should really have them analyze these outputs and, and, uh, give two pieces of information.
- 7:49
You should give a thumbs up or thumbs down. Is it... Was this response, uh, good? Was it bad? But more importantly, you should make that human annotator, um, perform a justification for why they chose that thumbs ur- up or thumbs down.
- 8:05
Reason being is that you, you're, you need to extract a lot of this domain-specific knowledge out of that human annotator's head so that eventually you can scale that type of knowledge through a, through a technique like, like LMS Judge.
- 8:18
But this is a great first step, performing human, human annotation. Who, who in this room is like at, at, at this step?
- 8:26
We're... Okay, this is way more advanced group. That- that's okay. That's okay. That's a good place to start. Are you using like human expert, uh, an- annotators?
- 8:34
I, I've just got like a... I mean, I, I haven't got any infrastructure set up.
- 8:38
Yeah.
- 8:39
Just, uh, yeah, it's pure run an agent on my data, see-
- 8:44
Yeah, see how it looks. Yeah
- 8:46
... create a prompt, and then repeat.
- 8:47
Yeah, totally. That's... It's, it's, it's you have to start somewhere. It's a great place to start. Um, uh, this is, this is how like that, that workflow is gonna, is gonna look.
- 8:55
You have a trace come in, thumb up, thumbs up or thumbs down, and then you add some justification, um, to that, so that eventually you can use it as, as an LMS Judge score down the line.
- 9:09
Um, this is like what this might look like in a, in a platform like Braintrust. Um, [chuckles] uh, we have like a, like a human annotator view, uh, built into the platform.
- 9:19
We actually let you vibe code your own annotation views. Um, important point, don't give a generic, uh, a- annotation platform to users. Really make it very specific to them.
- 9:32
They're gonna have an idea of how these agent traces should look, so you should deliver that to them, and it'll encourage them to, um, evaluate these, uh, these appropriately.
- 9:43
Um, okay. The ne- the next part is expanding upon that a bit, where now I just don't... I don't have only some human grader giving thumbs up and, and thumbs down and justification.
- 9:55
Now I'm starting to use those justifications, and I'm, I'm probably, um, running those justifications through a, a cursor cloud code or, or Codex to try to derive the actual failure modes of why when they gave a thumbs down, why they delivered a thumbs down.
- 10:12
Though these, you, you're, you now know and understand the failure modes of your agent.
- 10:17
Now that you understand the failure modes of your agent, you wanna be able to scale that human knowledge and be able to, to automate it so that you're not dependent on, uh, a few people with expertise to judge a agent, uh, agent outputs.
- 10:31
Uh, a couple ways to, a couple ways to do this, one of which using LMS to judge, uh, other LMS, LMS Judge. We, we've... That, that concept's been around for, for quite some time.
- 10:41
Very effective. Um, important here is that whenever you use an LM Judge, just because you put a robe and a cloak on an LM, that doesn't make it inherently more trustworthy.
- 10:53
You should be evaluating LMS as Judge outputs as well. Um, that's not really covered in this presentation, but, um, you should not just judge, uh, LM Judges blindly in that regard.
- 11:05
Uh, there also might be some objective failure modes where you can deterministically, um, encounter them just through code. That's okay too. You don't have to use LMS to judge other LMS.
- 11:16
You can use code to understand, um, if you're using too many tool calls, you might wanna fail that eval as an example. If you're using too many tokens, you might wanna fail that ev- uh, eval.
- 11:27
Um, I think the most important point here is that this dataset, uh, that's, that's on, on the right-hand side of this slide, at this point, you should probably be gathering production traces or at least, uh, UAT level traces into that evaluation dataset.
- 11:45
We want it to be ver... Uh, like don't think about evals as running tests. Think about evals like rerunning production because ultimately we wanna be confident as we run, uh, run these workloads in, in production.
- 11:58
Great way to do that is just to capture production data.
- 12:02
Um, most important point is, is this. Uh, we, we call it like the, the flywheel internally. We wanna be able to capture these traces, these agent traces in production Understand what's going wrong with them, either through a human or, or through automated tooling, um, and then bring those examples back to some offline experimentation environment, rerun production
- 12:27
through an eval, and then use that to guide us to which direction we should be improving our agent. So evals, that-that's, like, more playing offense with, with your evals.
- 12:39
Um, this is just, like, a, an example of, uh, setting up an, setting up an LLM as, uh, as judge scoring function to e-expand, um, your, your ability to evaluate at scale rather than using just a human.
- 12:54
Um, okay, uh, level two. Now we're starting to not just do simple model calls. We might be performing work with external systems. I think of, of tool calls in two different ways.
- 13:07
There's context gathering tools that are just, uh, gathering data and injecting that into the LLM, and then there is CRUD-based tools where you're creating, reading, updating, or deleting information from a database or an external system.
- 13:21
Um, both of these are, uh, can have a lot of lift in terms of whether your agent is quality or not. It also means that there's a lot of other things that can go wrong with your agent when you're starting to interact with external, uh, external systems.
- 13:38
Often now, instead of just having one, uh, um, uh,
- 13:42
of evaluating one specific part, i.e. the output of an agent, now you might be having to evaluate the entire trace of an agent. So, um, in that sense, t- like, this is where tooling starts to come into play.
- 13:56
You'll, you'll need some way to capture these large traces, understand each and every step that an agent took to be able to introspect and eventually target evals towards maybe even individual tool or MCP calls, uh, that your, that your agent is creating.
- 14:14
The other, the other problem here that, that we might have is, um, when you're performing CRUD on, on a system, you c- you don't really wanna do that when you're offline, of course.
- 14:25
There might not be a way to do that when you're offline. So y- when you run an eval, um, there's, there's two things that are problem areas. One, really challenging to represent the state that, that, um, uh, other external systems were, were in at the time that eval input was created, and then two, it makes it really
- 14:46
challenging to interact with those systems that the agent could be interacting with because you don't wanna overwrite any, any production data. These are real challenges that, that we have to solve for.
- 14:56
I would say it's not completely solved right now. Um, however,
- 15:01
um, there, there does need... There, there are some ways where you can represent external system state and interact with, like, mock level, uh, APIs so that you can approximate, um, real...
- 15:13
a real production environment in, um, as, as you're running evals. Um, the idea for this is that, um, these, these agent traces can be arbitrarily large. Um, in that sense, it's, it's a lot different than application tracing.
- 15:29
So if a trace can be arbitrarily large, you can actually cram in a ton of context, i.e. system state, the state that the external systems were in at the time, into these traces and inject that into a, i-into the task that you're running the eval upon.
- 15:46
In that way, uh, instead of having to, uh, um, uh, create an en-entire test structures and, and, and infrastructure, you can represent a lot of that stuff within the trace itself and encapsulate it there.
- 16:01
The other thing that you can do is you can use, like, really s- really specific querying techniques to, um, perform timestamp queries, uh, to systems that support them. So if an input came, um, and, and you added it to your dataset at a certain point in time, perhaps, uh, the way that you've set up your vector database,
- 16:22
you can run a version query to query the vec-vector database at a certain point in time, so that way you're adequately representing the state of, uh, of, of when that task ran originally.
- 16:35
These are more complex techniques, um, but ones that, ones that are a little, uh, that are a little bit more emerging. Um, I only have about two minutes left to go.
- 16:47
Um, what's next? Um, performing topic modeling at scale to make sure that you're uncovering those failure modes automatically in production. Um, that's something that, like, more than happy to talk about at, uh, at the booth downstairs.
- 17:00
And then of course, performing evals in a way where you're, um, using Claude Code and the eval provider CLI to be able to do this in an automated, uh, automated way.
- 17:11
These are two other patterns that, uh, that I see emerging in the space. I wanna be conscious of time. I probably have time for, like, one question, uh, before, before I have to jump here.
- 17:20
Is anyone curious about anything specifically? Otherwise, uh, you can find me at, at the booth. Yes, sir.
- 17:25
In our sphere, like, it's kind of normal to put a bit more respect on deterministic evaluation-
- 17:33
Yeah
- 17:33
... rather than deterministic graders. Do you agree with it? Do you think that we should push for more deterministic graders in this, in our eval platforms, or do we embrace LLM-as-a-judge as a solve?
- 17:45
I would-- Some things are subjective. That's why we love agents so much. I would embrace LLM-as-a-judge, but also perform a lot of evals on the LLM as, as judge so that, like, it's, it's very aligned with what a human would decide in the same circumstance.
- 17:59
You would eval the eval as a you but-
- 18:01
Yeah. It's easier to do that because LLM judge outputs are gonna, are going to be, uh, discrete, so you can create a ground truth dataset for that. Yeah. All right, everyone, I have to jump.
- 18:11
I'm at my time. Um, it was a pleasure to be with you all. And yeah, feel free to find me in the booth downstairs. [audience applauds] [outro music]