Ten runnable JavaScript experiments on agent decisions, each pairing a small Jev judgment with visible application policy code, plus authored fixtures so the whole set runs offline.
ReallyArtificial/jev-by-example
Organization repository on GitHub
Ten runnable Jev examples for agent decisions: memory conflicts, tool-result checks, recovery, context selection, and handoffs. JavaScript, zero dependencies.
Repo stats from the GitHub API, cached Oct 2026.
Should a new memory replace an old one? Did a successful tool call actually finish the task? Is a retry about to create the same invoice twice? Ten runnable experiments in building agents that know what to do next—and when to ask. Each combines a small Jev judgment with application logic you can read, change, and test. By Really Artificial · JavaScript · Node.js 20.17+ · Zero dependencies · MIT.
Quoted from the project's README. Read the full README.
Watch out
The headline result is not a Jev result. The README is explicit that a 34/34 fixture pass is a local software check, because the responses were authored to illustrate the policies rather than obtained from the model, and that live model behavior has not yet been verified. Fixture runs exercise request shapes, parsing, policy branches and reporting but establish nothing about accuracy, latency, calibration or cost; live runs add elapsed time and token usage and still establish no general accuracy or production-ready threshold. The thresholds in each policy are teaching choices rather than validated operating points, and a high confidence value does not certify correctness. A score value can be fractional because it is the probability-weighted position across levels, so the same mean can hide different uncertainty. The shipped cases contain fictional data, and substituting your own sends that state to TypeSafe on a live run. The code and docs were substantially authored with an LLM. Copy reviewed against the repository README on 2026-10-04, not from running it.
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