build vs buy for agent evals

The Part You Build Either Way

The expensive part of evals is not code. It is deciding what "good" means for each agent, writing that as a rubric a model can apply, labeling enough sessions to know whether the judge agrees with a person, and deciding what happens when a score drops. That work is identical whether the judge runs in a script or a platform.

• A definition of done for each agent: what it is for, what it must never do.
• One rubric per criterion, pass or fail, with a passing and a failing example.
• Fifty to a hundred labeled sessions to calibrate the judge against.
• A decision about what a low score triggers: a ticket, an alert, a rollback, a policy.

Built-in metrics shorten the first step, not the rest. Galileo (now Splunk Agent Observability) ships more than 20 out-of-box evals, Future AGI more than 50 metrics, DeepEval a long list of agentic ones such as Task Completion and Tool Correctness. Each still needs checking against your own labels before you trust it on your agent. Failproof AI has no metric library at all: you define its code checks and LLM judges, or bring the eval set you already have, so on Failproof AI this part is explicitly yours.

What a Platform Adds

• Capture and storage. Every session, tool call and result, queryable for months. Several platforms that publish their stack - Langfuse, LangSmith, Future AGI - use ClickHouse for it.
• A place to read sessions. The transcript, the score and the judge's reasoning on one screen. Most debugging happens here.
• Scoring on a schedule. Every session or a sample, retried when the judge times out, without a cron job you wrote.
• Charts across agents and environments, so a regression in production shows up beside staging.
• Alerts and issues. Thresholds on scores and error counts, routed to whoever is on call.
• Access control and retention. SSO, roles and deletion, which a security review will ask for.
• Population analysis and runtime action, in some products: audits across many sessions, and policies that stop an action before it runs.

Of that list, the item teams underestimate is the session UI. Most eval work is not reading numbers; it is opening the session behind a low score and working out whether the agent or the judge was wrong. A home-built pipeline usually has the numbers and a query window, and every investigation starts with someone writing SQL against JSON. The item teams overestimate is the judge runner: a queue and a few processes calling a model is modest code. If your team already runs a warehouse and a BI tool, you own half the plumbing, and the case for building gets stronger.

What Building It Costs

The line people forget is maintenance. Harness event formats change, judge models get upgraded and move scores, someone asks for a customer's data to be deleted, and a security reviewer asks who can read production transcripts. Each is a small task. Together they are a part-time job that never appears in the build estimate.

The line that is the same both ways is judge tokens. Whichever you choose, a model reads every transcript a judge scores, and someone pays for those tokens - you directly, or the vendor through its price. How much agent evals cost has the arithmetic.

A Decision Rule

Count how many of these are true today.

1. More than one agent is in production, or more than one team owns agents.
2. Sessions arrive faster than anyone could read a weekly sample by hand.
3. Someone is on call for agent behavior, not just uptime.
4. A security review will ask for SSO, roles, retention and audit logs.
5. You need to stop a failure, not only measure it.

Zero or one: build, and keep it small. Two or three: self-host an open-source platform or buy an entry tier. Four or five: buy, and spend the time you saved on rubrics and labels, which is where eval quality actually comes from.

The Middle Paths

Self-host an open-source platform. Langfuse and Latitude are MIT and free to run, so you buy nothing and build only the operations. You still patch, back up and scale the databases, and some features - audit logs in Langfuse, for example - need a license even on your own servers.

Buy a platform that runs the suite you already have. Keep the judgment in rubrics and code you own, and rent the storage, charts and alerts around it. Failproof AI works this way. It runs evaluations in the cloud: code checks the assistant drafts from a plain-English description, tested on real sessions and versioned so you can roll back; LLM judges; and the eval set you already have - DeepEval, Ragas, promptfoo or your own - brought in as it is. Each result lands as a score, metric or assertion beside the trace. Moving an in-house suite in does not mean rewriting it, and if you leave, the judge logic leaves with you, because it was yours to begin with.

When Building Is the Right Call

Build when evaluation is a research problem for you rather than an operations one - you are inventing metrics, not running them. Build when your data cannot leave a network no vendor will deploy into. And build when the whole need is a CI gate on one agent: a script, a frozen dataset and a threshold is about a day of work, and a platform would be overhead. Do we need an eval platform? has a version of that script.