Introduction
A client we worked with last year built an AI agent for procurement requests. It parsed supplier documents, compared pricing, flagged compliance risks, and drafted recommendations. It worked well. But a couple of months into production, the team hit a question they did not have a good answer to: how do we measure whether the outputs are consistently good?
They were doing manual spot-checks on a few cases per week. The compliance flags were firing on some requests and not on others that looked similar. The pricing comparisons were mostly accurate but occasionally off by margins that mattered. The agent was delivering value, but the team had no systematic way to quantify how much, or to track whether quality was improving or drifting.
This is a familiar moment in agentic AI adoption. The system works well enough for real tasks, and the organization is ready to move from “it works” to “we can prove it works.” That transition requires a different kind of evaluation than what most teams are used to, and it is one of the most important capabilities organizations are building right now.
Why agents need their own evaluation approach
Traditional ML had clean metrics. Precision, recall, AUC. Even early generative AI could lean on human preference ratings and benchmark datasets. Agents are different enough that those approaches do not transfer directly.
The main reason is that agents operate across multiple steps. They make branching decisions, call tools, recover from errors, and produce outcomes that depend on the path they took. Run the same agent with the same input twice and you may get different tool call sequences, different intermediate reasoning, and different final outputs – all of which could be perfectly valid. An agent that solves a problem through a different sequence of steps than expected is not wrong. It may even be better.
This means evaluation needs to evolve. At Artefact, we have been working on practical approaches to this across our agentic engagements, and a clear set of patterns is emerging that works well in production.
What makes agent evaluation different
There are three things that make evaluating agents distinct from evaluating models.
The first is non-determinism. In classical ML, you evaluate a model against a test set and get a stable number. With agents, the output depends on a chain of LLM calls, tool invocations, and branching decisions. Asserting that “the agent must call function X before function Y” does not work when there are multiple valid orderings. Evaluation needs to account for multiple correct paths to the same outcome.
The second is path dependence. An agent’s final answer is the product of a chain of decisions, each influencing the next. Two agents can produce the same correct output, but one recovered from a tool failure along the way while the other happened to avoid it. Looking at the final output alone misses that difference. Understanding the reasoning path gives you a much richer picture of how robust the system is.
The third is context-dependent correctness. In many enterprise use cases, quality exists on a spectrum. A procurement agent that negotiates a 12% discount is good.
One that negotiates 15% is better. One that negotiates 10% but preserves a critical supplier relationship may be best of all. Useful evaluation needs to reflect the business context, not just whether the output is right or wrong.
Approaches that are working in production
Some approaches are working well already, and the common thread is treating agents more like systems than like models.
Trajectory-level evaluation is probably the most impactful shift. Rather than scoring only the final output, teams assess the full sequence of agent actions. Did the agent identify the right sub-tasks? Did it use appropriate tools? Did it recover from errors? Did it escalate when it should have? This requires structured logging of every agent step and rubrics that score the trajectory as a whole. It gives teams a much more complete picture of agent quality than looking at endpoints alone.
LLM-as-judge with calibrated rubrics has also matured quickly. Using a separate LLM to evaluate agent outputs is becoming standard, and the key to making it reliable is the rubric. Define explicit criteria, provide graded examples of good and bad performance, and regularly calibrate the judge against human evaluations. Teams that get the rubric right see strong correlation with human judgment.
Scenario-based testing is another approach that pays off well. The most effective teams I work with maintain libraries of 50 to 200 scenarios covering happy paths, edge cases, adversarial inputs, and known failure modes. These grow with every production incident and become one of the most valuable assets in an agentic AI program.
And continuous evaluation in production ties it all together. Pre-deployment testing gives you confidence at launch. Continuous evaluation gives you confidence every day after. Sampling production interactions, running them through evaluation pipelines, and tracking quality metrics over time lets teams improve agents systematically rather than reactively.
“Evaluation is not something you do before launch and move on from. It is an ongoing practice, and the teams that treat it that way are the ones shipping agents with confidence.” – Abhishek Singh, Data Science Director, Artefact
Getting the ownership model right
There is an organizational side to this that is worth addressing.
In traditional ML, the data scientist who built the model typically owned its evaluation. In agentic systems, the scope is broader. The agent touches multiple business processes, calls tools maintained by different teams, and produces outcomes that affect stakeholders across the organization. Ownership models for agent evaluation are still emerging across the industry, and getting this right early makes a real difference.
The teams we see doing this well establish a clear evaluation owner – not necessarily a dedicated team, but a person or function responsible for defining quality standards, building evaluation infrastructure, and holding development accountable to measurable criteria. Making ownership explicit early avoids the situation where everyone assumes someone else is handling it.
Practical recommendations
Based on what we have seen across engagements, here is what I would recommend for teams investing in agentic AI.
Start with the evaluation framework, not the agent. Before writing agent code, define what success looks like. What are the measurable outcomes? What does a good trajectory look like? What failure modes do you want to catch early? This upfront work avoids the common situation where teams build a capable agent and then spend months figuring out how to validate it.
Invest in structured logging from day one. Every tool call, every decision point, every LLM interaction should be logged in a structured, queryable format. Without it, you can only evaluate outputs without understanding how the agent arrived at them. Retrofitting observability later is more expensive than building it in from the start.
Build a scenario library and treat it as a first-class asset. Version it, review it, grow it with every production incident.
Plan for continuous evaluation from the beginning. And assign a clear evaluation owner early – the teams that move fastest from pilot to production are the ones where someone is explicitly accountable for agent quality.
How we approach this at Artefact
At Artefact, evaluation is built into our AI Factory methodology as a core workstream. Every agentic engagement includes an evaluation framework design phase that runs in parallel with agent development. We define trajectory-level rubrics, build scenario libraries tailored to the use case, and set up continuous monitoring before the agent goes live.
The thinking behind this is straightforward: an agent you can measure is an agent you can trust, improve, and scale. The organizations we work with that adopt this discipline early consistently reach production faster and with greater stakeholder confidence.
Evaluation as a competitive advantage
Agentic AI is maturing fast. Frameworks are better, models are more capable, and the use cases are real. Evaluation is catching up, and the organizations that invest in it now are building a genuine advantage.
Done well, evaluation is what lets you deploy agents with confidence, improve them over time, and show stakeholders evidence rather than demos. The discipline is relatively new, but the approaches are becoming well-understood, and the teams that adopt them early will be well positioned as agentic AI becomes a standard part of how enterprises operate.
