Agentic AI Workflows Can Drain Budgets Fast Through Token Overuse

Agentic AI workflows, while powerful, can rapidly escalate costs as multi-step loops generate numerous model calls and accumulate large input contexts with each turn. Files, logs, and tool schemas are often re-sent repeatedly, and retries late in a session become increasingly expensive due to growing context windows. This pattern, described as 'token addiction,' occurs when deterministic tasks are unnecessarily routed through an LLM instead of simpler logic. A more cost-efficient approach involves reserving AI for genuinely ambiguous tasks while using rule-based workflows for structured, high-volume, or repetitive operations. Experts suggest deploying full agents only when the solution path is truly unknown, and always with a token budget and a kill switch in place.