An Eval Suite Starter Kit for Your First Production Agent

Everything covered so far in this series — judge biases, trajectory grading, sampling architectures, agent-as-judge, guardrails, correlation validation — is real, and none of it is wrong to want eventually. But a solo builder or small team shipping their first production agent doesn't need all of it on day one, and trying to build all of it at once is a good way to spend three weeks on eval infrastructure before shipping anything a user can touch. What follows is the order this actually gets built in, by teams that end up with something that works rather than something impressive that never ships.

Week one: ten to twenty cases and a single-criterion judge

Start with the seed-case exercise from "Building a Golden Dataset When You Don't Have Real Traffic Yet": list every way your agent could plausibly fail, based on what it does and what tools it has, before you have a single real user. Ten to twenty cases is enough. Don't wait for more.

For each case, write one LLM-as-judge prompt that checks one thing — not a holistic quality score, a single criterion: did the agent complete the task, or not. This is the decomposition principle from "The Biases Baked Into Every AI Judge" applied at the smallest possible scale, and it's worth starting here rather than with a fuller multi-dimensional rubric, because a single binary criterion is something you can sanity-check by eye in five minutes and trust immediately. A fancier rubric you haven't validated yet is worse than a simple one you have.

Run this against your cases before you ship anything. This is the entire eval suite for week one, and it's already strictly better than shipping with no evals at all, which is the actual baseline most first agents launch from.

Week two: adopt the eval-driven habit, not more infrastructure

Before adding any new tooling, adopt the workflow habit from "Why Eval-Driven Development Is Replacing Vibe-Checking Outputs": every time something breaks — in testing, or once you have your first handful of real users, in production — turn that specific failure into a new case in your dataset before you fix it. Confirm it fails, fix it, confirm it passes, confirm nothing else regressed.

This is the highest-leverage thing available in week two because it's free — no new infrastructure, just a discipline — and it's the mechanism that turns your ten-to-twenty seed cases into a dataset that actually reflects your agent's real failure history rather than just your best guesses from week one. Most of the value of a mature eval suite six months from now traces back to this habit being followed consistently from week two onward, not to any single piece of tooling added later.

Week three or four: add position rotation and chain-of-thought to your judge

Once you're running your single-criterion judge regularly and it's caught a few real problems, invest a small amount of time hardening it against the two biases that are cheapest to fix and most likely to be quietly distorting your scores: position bias, if you're doing any kind of pairwise comparison (old prompt vs. new prompt, model A vs. model B), and verbosity bias, if your rubric has drifted from a strict binary toward anything resembling a holistic quality score.

Add chain-of-thought reasoning to the judge prompt — have it explain its reasoning before giving a verdict, not just output a score — both because it measurably improves reliability and because the reasoning trail becomes your first real debugging tool when a score looks wrong and you need to understand why, rather than just knowing that it is.

Month two: recruit five real users and validate against them

Once you have even a handful of real users, put the agent in front of five to ten people who've never used it, per the same piece on golden datasets, and turn what you observe into new cases — especially the confused or failed sessions, which are worth more than everything generated synthetically up to this point.

This is also the point to do a lightweight version of the correlation validation from "What Correlating Judge Scores to Human Ratings Actually Looks Like": you don't need a formal inter-rater study with a computed coefficient yet, but you do need to personally read through a batch of your judge's verdicts against the actual outputs and confirm you agree with most of them. If you don't, that's the signal to rewrite the rubric before trusting the judge further, not to add more infrastructure on top of a foundation you haven't checked.

Month three or later: trajectory grading, once you have tools worth tracing

If your agent has meaningful tool access — anything beyond a single lookup call — this is the point to start capturing full execution traces and writing the first trajectory-level checks from "Trajectory Evals: Grading the Path, Not Just the Answer," starting narrow: one check for unnecessary or redundant tool calls, one check for how the agent handles a tool error, rather than trying to grade the full trajectory holistically on day one. This is also the point where investing in proper structured logging of tool calls and intermediate state pays for itself — you can't grade a trajectory you never captured.

Only when volume actually demands it: the sampling architecture and guardrails

The two-tier cheap-heuristic/expensive-judge architecture from "Cheap Heuristics on 100% of Traffic, Expensive Judges on 10%" and the inline guardrails from "Turning Evals Into Guardrails That Run at Inference Time" are real infrastructure investments, and they earn their cost at a specific point: when request volume is high enough that running your full judge on every request is genuinely expensive or genuinely slow enough to hurt the user experience. For a small team with a few hundred requests a day, that point may not arrive for months. Building the sampling architecture before you have the volume to need it is effort spent on a problem you don't have yet, at the cost of time you could spend on a problem you do.

When it does arrive, the build order mirrors the priority: get a fast, cheap check running on everything first — even a simple schema or keyword check counts — before investing in the more expensive sampled judge tier, and only add hard-blocking guardrails for the narrow set of checks where a false positive is cheap and a false negative is expensive, like destructive tool calls or policy violations.

Agent-as-judge: last, and maybe never

Agent-as-judge is the most capable and most expensive tool in this entire series, and for a huge share of small teams and solo builders, it's reasonable to never need it. It earns its cost specifically for high-stakes, low-volume trajectories with externally verifiable claims — and if that description doesn't match anything your agent does, skipping it entirely is a legitimate, permanent decision, not a gap to feel behind on.

The actual point of a starter kit

The single biggest mistake available to a team reading this series is trying to build the whole stack before shipping anything — a full multi-dimensional judge, trajectory grading, sampling architecture, and guardrails, all before a real user has touched the agent. None of that infrastructure has anything to validate itself against until real usage exists, and building it all up front means every layer is a guess rather than a response to something that's actually happened. Ten cases and one honest binary judge, running consistently and growing with every real failure, will tell a small team more about whether their agent works than an elaborate eval architecture built in isolation ever will. Everything else in this series is what that starter kit grows into, in the order it actually gets needed — not the order it has to be built in before anything ships.

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Part of the "Evals for Agents" series on aiskill.market.