AI Cost for Agents
Agent cost is a workflow cost. Learn how to cap retries, prevent loops, and keep tokens under control.
Agent runs are where cost surprises happen: retries, tool chains, and self-reinforcing loops.
Why agents can burn budgets
- Tool retries multiply calls
- Long reasoning turns into more tokens
- Loops happen when there’s no convergence signal
Cost breakdown
Agent cost = tokens across all model calls + tokens generated by tool context + retries.
Real example
An agent repeats a tool call sequence because the result never satisfies the stopping condition.
Optimization guardrails
- Cap retries and depth
- Add a convergence rule (stop when you reach the goal)
- Set per-agent budgets and alerts
Quick checklist
- Stop runaway loops
- Use routing for simple steps
- Track agent cost by run
Related reading
Lower cost without hurting quality: fewer passes, tighter prompts, and smarter context selection.
Keep unit economics stable with per-tenant cost controls, routing, and measurable cost reporting.
Reduce AI API cost early: budget, token discipline, and agent guardrails that don’t slow you down.
A structured approach to reduce AI API cost across models, prompts, tools, and monitoring.