That is embarrassing onstage. In production, it is a reliability problem: a workflow that succeeded once may fail the next time a user makes the same request. For mission-critical work, such as reconciling a financial transaction or checking a contract for an obligation, that can be a showstopper.
Most benchmarks hide this variability behind an average. On AppWorld, a ReAct agent using GPT-4.1 succeeded on 77.4% of runs across five repetitions. But it succeeded in all five runs for only 53.0% of tasks — a 24.4-point consistency gap.
Most benchmarks report the first number. We built a way to measure the second — and improve it.
In an earlier post, we introduced ALTK-Evolve — a system that turns an agent's own past trajectories into reusable guidelines, distilled automatically and injected back at inference time. It measurably improves task success, but those results only asked the average-case question too. This post introduces consistency guidelines, a new guideline type in altk-evolve built on top of a diagnostic tool we call the Consistency Analyzer, that targets this gap directly.
Standard agent evaluation reports Mean@k: run a benchmark k times, average the pass rate. Often k=3, sometimes just 1. It's the number on every leaderboard, and it's what "77% accurate" means in practice.
" It does not answer the question a real user cares about: will it still be good if I ask this exact question again? For that you need Pass^k : the fraction of tasks where the agent succeeds on all k runs.
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