Overview & Result
Claude Opus 5.5やGPT-6 Solの登場で、主要AIモデルの世代交代と激しい価格破壊が一気に押し寄せました。判定特化の「Jev」など新潮流から運用の落とし穴まで、目まぐるしく変わる開発現場のリアルを僕の視点で紐解きます。 https://t.co/0ovmTAg7Ju
How Jev fits in the loop
- Ingest runtime state, user inputs, and environment context into a typed Jev evaluation contract.
- Jev evaluates candidate branches or safety gates in single-digit milliseconds with deterministic probability outputs.
- Downstream agent loop immediately routes execution to specialized tools or frontier models without stalling in generative loops.
How to reproduce
- Review original technical breakdown and architecture thread by @fujikawa at https://x.com/fujikawa/status/2102529646210474478.
- Configure Jev SDK client with strict schema constraints matching this domain's state transitions.
- Benchmark end-to-end latency and error rate before and after inserting Jev as the decision layer.
Why this build matters
Replaces expensive, high-latency frontier LLM calls with microsecond typed decisions, cutting inference costs by up to 90% while ensuring strict state machine determinism.
Limitations
- Decision schema requires explicit predefined candidates; not suitable for unbounded freeform prose generation.
- Third-party API rate limits and upstream network latency bound total end-to-end responsiveness.