For most of software engineering history, “is it working?” had a reasonably clean answer: the service is up, requests succeed, latency is within budget. That answer breaks down for AI agents operating in high-stakes domains — and recognizing where it breaks is the starting point for building something better.
The monitoring blind spot
An AI agent can produce a confident, polished response that says exactly the right thing — and still fail completely. A banking support agent can tell a customer “your refund has been issued” without ever calling the tool that issues refunds. Infrastructure metrics stay green. Error rates stay flat. The failure surfaces days later when the customer follows up.
This is not a corner case. Non-deterministic systems (systems whose outputs are not fully predictable from their inputs) require a fundamentally different approach to quality assurance than deterministic ones. Uptime and latency tell you nothing about whether the agent is doing the right thing.
What a proper eval loop looks like
The practice that has emerged in production AI engineering involves five interconnected layers, each feeding the next.
Tracing is the foundation — capturing every step an agent takes: every model call, every tool invocation, the inputs and outputs at each stage, latency, token counts, cost. Without a full trace, there is no raw material to analyze.
Monitoring aggregates traces at scale to surface signals: cost spikes, guardrail violations, user frustration patterns. The signals worth watching are often indirect — a customer typing in all capitals is a different kind of failure signal than a failed API call, but it is still a signal.
Datasets are the closest analogue to unit tests in traditional software. A dataset is a curated collection of (input, expected behavior) pairs that codifies what “correct” looks like for a given use case. Expected behavior does not need to be a literal string match — it can be a keyword, an absence (the agent must not offer investment advice), or a quality dimension.
Experiments let practitioners change one variable at a time — a prompt wording, a model version, a tool description — and measure the effect against the dataset. Isolating the variable is what makes results interpretable rather than anecdotal.
Evaluators score each trace automatically. Two types are worth combining: deterministic evaluators (exact match, regex — fully consistent, no noise) and LLM-as-a-judge evaluators (flexible, able to assess nuanced quality dimensions, but with non-zero inaccuracy). Relying entirely on LLM-as-a-judge introduces noise; anchoring with deterministic checks keeps the baseline reliable.
Building ground truth in regulated contexts
The hardest part of this loop is building the dataset — the ground truth that everything else is measured against. Three patterns have emerged for organizations in regulated industries.
Expert curation starts from scratch: domain specialists define test cases, often in a spreadsheet before any tooling is involved. Shadow runs place the agent alongside an existing manual process, comparing outputs to build ground truth from real operational data. Human-in-the-loop automation starts fully manual and progressively automates the cases where confidence is high, leaving human review for the edge cases.
None of these is fast. All of them are worth the investment, because a dataset is not just a testing artifact — it is a codified understanding of what the system is supposed to do.
Gating deployments on evidence
The loop closes when eval results gate deployments. Each prompt change or model update triggers a run against the dataset; scores are compared against defined minimum thresholds; the result is a pass or fail attached to a specific configuration. The alternative — promoting changes based on informal judgment — is difficult to audit, impossible to reproduce, and tends to accumulate debt quietly.
For industries where decisions have real consequences — financial advice, medical information, legal guidance — this kind of evidence trail is not a nice-to-have. It is the mechanism that makes it possible to know, not just believe, that guardrails hold.
Where this is heading
Eval infrastructure for AI agents is still maturing, but the direction is clear. As agents move deeper into regulated domains, the question will shift from “does this agent produce good outputs in demos?” to “can we demonstrate, systematically, that it behaves within defined limits across a representative range of inputs?” The teams building that answer now are accumulating an advantage that will compound.
Text summarized and optimized using Anthropic’s models and reviewed by a human.