Agent Economics

Agent Economics refers to the cost structure, efficiency logic, and economic trade-offs involved in operating AI agents in production systems. Unlike traditional software costs, agents generate variable per-task operating costs: every agent run consumes tokens, fills context windows, and triggers inference charges — often across many model calls, tool invocations, and reasoning steps. A core concept in agent economics is the cost-per-task metric, which captures an agent's total resource consumption across a complete work cycle. This replaces the simpler cost-per-API-call metric common in non-agentic AI systems, since a single agent run may involve dozens of model calls. Key design levers that directly affect cost include model routing (directing simpler sub-tasks to cheaper models) and context budgeting (limiting the context window per step to reduce token consumption). As AI agents become standard in developer teams — handling code review, documentation, and autonomous testing — agent economics is becoming a core operational discipline. Teams that deploy agents without cost controls risk unbounded token growth. Those that systematically apply routing strategies, context limits, and task decomposition achieve significantly lower costs without sacrificing output quality. Agent economics therefore shapes not just the finance of AI, but which agent workflows are practically deployable and scalable at the enterprise level.