Measuring an AI workflow: cost per accepted outcome, including rework
How to measure an AI workflow by cost per accepted outcome, with rework, retries and operating effort included and compared with the previous process.
Field notes
Production AI patterns we ship every week. Stuck pilots, MCP hardening, eval suites, agent orchestration, cost control. Written by Alex Cinovoj, Founder & CTO, TechTide AI · CTO & Co-Founder, CPG Playbook. No vendor pitches, no recycled blog posts.
5 of 71 field notes · 2026 archive
How to measure an AI workflow by cost per accepted outcome, with rework, retries and operating effort included and compared with the previous process.
A proposed baseline-first measurement method: one workflow, consistent definitions, and operating costs reported alongside the measured benefit.
Leaderboards measure someone else's problem. Build a 50-task set from your own traffic, score it blind, price it per task, then keep the swap cheap.
Every long-context release restarts the RAG obituary. Retrieval still wins on cost, freshness, permissions, and citation. The five fixes that rescue a weak pipeline.
Nobody plans an AI budget overrun. They just never set a ceiling. Per-task budgets, tiered routing, context hygiene, caching, and one dashboard with a named owner.
Buying guides on alexcinovoj.com and engineering notes on alexcinovoj.dev, published alongside these field notes.
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