What one Jev decision looks like inside a 10,000-decision agent
Jev turns "who goes next" into a typed Choice with a confidence score; 0.88 clears the 0.85 threshold and auto-approves, and 10,000 such decisions cost about $0.42.
this is what one decision looks like inside a 10,000-decision agent your chief of staff asks "which worker goes next?" hundreds of times a day. most people pay a frontier model to answer that. Jev answers it as a typed choice with a confidence score: goal: compare 3 AI-agent tools choice: research confidence: 0.88 above the 0.85 threshold cost: $0.000042 status: saved queue/research below 0.85, it goes to review. no guessing, no paragraph to parse. at $0.042 per 1M input tokens with free output, 10,000 of these cost $0.42. the LLM does the work. Jev decides where the work goes. code executes. full setup + the chief. py router in the article ↓
This post quotes another post: “Long read: how to build a Jev-powered agent stack”