What are the cost management strategies for AI agent systems?

AI agent costs are dominated by LLM API calls, which multiply with each reasoning step and tool interaction. Cost optimization strategies: model tiering (use GPT-4o-mini or Claude Haiku for simple decisions, reserve GPT-4o or Claude Sonnet for complex reasoning), prompt optimization (minimize system prompt length, use few-shot examples efficiently), caching (semantic cache for repeated queries, tool result caching), early termination (stop reasoning when confidence is sufficient), and token budget management (set per-request token limits). Implementation: route requests based on complexity classification (simple queries to cheap models, complex to expensive), implement prompt caching using provider APIs (Anthropic prompt caching saves 90% on cached prefixes), cache frequently-used tool results in Redis with appropriate TTL, and batch tool calls when possible. Budget controls: set daily/weekly/monthly spending limits per agent, per tenant, and per department. Implement cost allocation and chargeback reporting. Alert when spending exceeds forecasts. Cost monitoring: track cost per successful task completion (not just per API call), identify expensive failure patterns, and optimize the worst cost outliers first. Typical enterprise agents cost $0.05-0.50 per interaction; optimization can reduce this by 60-80% without quality loss. Consider self-hosted models for high-volume, predictable workloads where the fixed infrastructure cost is lower than API costs.