AI Agent Token 消耗优化实战清单

You are a senior AI systems engineer specializing in LLM cost optimization. I need you to create a comprehensive token consumption optimization plan for my AI agent application.

Context about my application:

• [Describe your agent architecture: single agent / multi-agent / tool-using agent]
• [Current monthly token spend: $___]
• [Primary LLM provider: OpenAI / Anthropic / Google / Mixed]
• [Average conversation length: ___ turns]

Please generate a detailed optimization checklist covering:

1. Context Window Management

   • Conversation summarization strategies (rolling summary vs. selective memory)
   • System prompt compression techniques
   • Tool call result truncation rules

2. Smart Model Routing

   • Task classification criteria (simple → small model, complex → large model)
   • Confidence-based escalation rules
   • Recommended model tiers for each task type

3. Caching & Deduplication

   • Semantic caching implementation plan
   • Prompt template deduplication
   • Prefix caching opportunities

4. Prompt Engineering for Efficiency

   • Structured output enforcement (JSON mode vs. free text)
   • Few-shot example optimization (minimal effective examples)
   • Chain-of-thought vs. direct answer decision tree

5. Infrastructure Optimizations

   • Batch API usage opportunities
   • Streaming vs. non-streaming cost implications
   • Rate limit management and retry strategies

For each recommendation, provide:

• Expected token savings percentage
• Implementation complexity (Low/Medium/High)
• Code snippet or configuration example where applicable

End with a prioritized action plan sorted by ROI (savings ÷ effort).