Methodfield has published a five-part AI Systems series for teams deciding what AI work should cost, which model should do it and when an agent is justified. It follows one fictional retailer's customer-service workflow from a single model call to a controlled operating system. The example illustrates the method; it is not a reported client result.
- Is the token the new currency of AI? — move from API consumption to cost per accepted outcome.
- A context window is not free memory — compare full context, retrieval, summaries and caching on actual work.
- Choose a model for the job — use task categories, quality gates and routing before comparing tariffs.
- What does AI efficiency actually mean? — evaluate quality, elapsed time, human burden, risk and full cost together.
- The full cost of an AI workflow — budget tools, loops, retries, review, recovery and governance.
Each article includes a visual model, linked primary sources, a decision method and a route to the next chapter. Public API prices change, so the operating method is intentionally more durable than a price list. The knowledge map connects the series to the wider AI Systems collection.