Build or Buy the Eval Harness? What Each Choice Locks In

Eval tooling is where teams most often buy something and keep using their own scripts anyway. The reason isn't tool quality — it's that agent evals depend on details of your loop that a general-purpose harness can't reach, and the decision hinges on which of those details you have.

What a harness actually has to do

Four jobs, and their difficulty is wildly uneven:

1. Run cases and collect outputs — easy either way.
2. Score them — deterministic assertions plus, often, a model judge.
3. Deterministically replay tool calls — hard, and coupled to your code.
4. Report and compare across versions — tedious, valuable, and genuinely commoditized.

Jobs 1 and 4 are what bought tools do well and what teams underestimate the cost of building. Job 3 is where bought tools tend to stop fitting.

The replay problem

An agent eval that hits live tools isn't a test — it's an integration run whose result depends on today's data. To compare two prompt versions you need identical tool responses, which means recording real responses and replaying them.

Replay must intercept at your tool-dispatch layer, and that layer is yours. A harness can support it only through an interface you implement, which puts you back in the business of writing the coupling code — the part that's actually hard.

⚠️ This is the specific reason bought harnesses get half-adopted: the reporting is used, the running is used, and replay quietly stays in a local script because nobody could make it fit.

The corollary is a good design instinct regardless of what you buy: make tool dispatch a seam you control, one function that everything routes through. Then replay is a swap at that seam, and any harness can drive it.

What building gets you

• Assertions in your own language, against your own trace format, with your domain vocabulary.
• Replay that matches your loop exactly.
• No mapping between your concepts and someone else's schema.

What it costs, in the order the cost arrives: a trace format you must version; a results store; a comparison UI or the discipline to live without one; flake handling; and CI integration. None of it is deep work. All of it accumulates, and the reporting layer in particular is a permanent tax that never becomes interesting.

What buying gets you

• Reporting, diffs, and history from day one — the thing teams most reliably fail to build.
• A trace/eval schema someone else maintains as providers change.
• Regression tracking across versions without you owning a database.
• Usually, a judge implementation with the standard bias mitigations already applied.

What it costs: your traces must be expressible in their model. Multi-agent runs, custom step types, and unusual control flow are where the mapping strains. There's also a real risk of shaping your agent to fit the harness's assumptions — which is fine when its assumptions are good and quietly limiting when they aren't.

The decision

• Assertions are mostly deterministic (tool called, argument correct, schema valid) → building is cheap; you mainly need a runner and a table. Consider buying only for the reporting.
• You need judge-based scoring with calibration → buying saves real work, since the bias mitigations are fiddly and easy to get subtly wrong.
• Your agent's structure is unusual — deep multi-agent, custom step semantics — → build, or accept a lossy mapping.
• Multiple teams need to compare results → buy, or you'll build a reporting product by accident.
• Compliance requires audit retention → check retention and export terms before deciding; this constraint outranks the others and is easy to discover too late.

✅ The hybrid most teams converge on

Own the case definitions and the assertions — they're your requirements, in your repo, in code review, versioned with the agent. Own the seam that makes replay possible. Buy or borrow the reporting and history layer, because comparing runs over time is real engineering with no domain value.

The thing to keep out of any external tool: the eval cases themselves. They're specification. They belong next to the code they describe, reviewed like code, so that a PR changing behavior changes its cases in the same commit.

The takeaway

The build-or-buy line for agent evals falls in an unintuitive place. Scoring and reporting are commoditized; deterministic replay is coupled to your loop and always partly yours. Design the seam first, keep the cases in your repo, and buy the layer whose value is history rather than correctness.