AI Agent Token Consumption Optimization Practical Checklist

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).