Cost optimization patterns for autonomous agent runs
How to manage model spend with budgets, route selection, caching, retry policy, and per-agent cost attribution.
Agent cost is not just token count. It is retries, tool fanout, evaluator passes, long context, duplicated retrieval, and human waiting time.
AstraRunner treats this as an execution-platform concern: the run needs durable state, traceable decisions, cost visibility, and enough structure for teams to review what happened after the agent finishes.
Patterns that work
- Set per-team and per-agent budgets before the run starts.
- Route simple steps to smaller models.
- Cache deterministic tool reads and retrieval results.
- Stop retry storms with attempt budgets.
Implementation checklist
- Attribute spend to run, role, workflow, and artifact.
- Separate model spend from tool spend.
- Show budget remaining during execution.
- Alert on abnormal fanout.
Watch outs
- Monthly cost review is too late for autonomous loops.
- Evaluator passes can cost as much as generation.
- Retries can multiply spend without improving outcomes.
Related reading
How this fits the Astra stack
AstraRunner owns the operational path for agent work: orchestration, scheduling, approval gates, traces, costs, and role-agent handoffs. AstraMemory owns durable context and retrieval. AstraGenie turns those capabilities into product workflows for teams that want automation without rebuilding the platform layer.