Ways to reduce token cost in AI agents
Ive been building AI agents for a while and noticed a few patterns that help cut token usage without wrecking the workflow.
A few things that seem to matter most:
- Set hard token budgets per task or step.
- Stop runaway loops early with guardrails or circuit breakers.
- Use smaller models for cheap steps and reserve larger ones for harder reasoning.
- Summarize context aggressively instead of carrying the full history forever.
- Track token spend per workflow, not just per request.
- Add safety checks so bad prompts or tool loops don’t burn budget.
I’ve been working on a small npm SDK called agent-cost-controller that wraps some of these ideas into one place for Node/JS agent apps. But I can't find anything useful for Python.
Curious what else people are doing to keep agent token usage under control, especially in production