Agent Cost & Model Selection Series — Start Here
This hub collects writing on agentic cost engineering: choosing agents and models for ROI. The public site one-liner stays broad; this cluster is where the niche gets sharp.
It is not an OpenRouter-only story. OpenRouter is one control plane I use in practice. The ideas — task classes, capability labels, failure-priced ROI — apply with any router or even a static allowlist.
Core notes
- Operating Note 002: Right agent, right model, best ROI — positioning + A–D task classes
- Automatic model watch — discover → test → label → promote
- When cheap models get expensive — price failure into ROI
- Routing tables for content and data agents — lane tables you can adapt
- Eval fixture pack (copy-paste) — stable battery for promote/demote
Worked examples in the wild
- Cut Cursor costs with OpenRouter routing — IDE lane table
- Autonomous content org on €35/mo — infra band + approval gates
- AI agent stack as competitive edge — producer vs critic models
Operating backdrop
- Company OS — intent, approvals, outcomes
- What is agentic engineering?
Planned next
- Spend budgets as Company OS policy per agent role
- Domain-specific fixture packs (content voice, warehouse SQL)
Hire / discovery if this is your bottleneck: /agentic-engineer-stockholm · /hire