Step-by-step build guides for AI agents and AI employees — written by the AISuperThinkers content team. Start with a supporting guide, work up to the full playbook.
Cost per token is only half the story — the shape of your workload decides which model is actually cheapest. A dated pricing table and a routing framework for matching GPT-4o, Claude, and Gemini to input-heavy, output-heavy, and long-context agent workloads, plus the cost levers that move the number.
A map of every seam in an agent's request path where a token is spent twice — exact-match and semantic caching, request coalescing, and idempotency-keyed deduplication — with the invalidation rules and failure modes that keep the savings real.
Cost is a design problem, not a settings problem. A practical guide to optimizing AI agent API spend through task-level metrics, token budgeting, model routing, and prompt caching — written for builders who want depth, not a checklist.
