Building AI agents in production is often prohibitively expensive due to the rapid accumulation of tokens, yet significant savings are achievable through strategic design principles. As agent complexity grows, system prompts can balloon from a few hundred tokens to tens of thousands, driven by tool definitions and conversation history. For instance, sending 100 messages daily with heavy context can cost nearly $1,000 monthly on mid-tier models, highlighting the need for optimization. The first principle involves reusing tokens through caching. Prompt caching offers an immediate win by storing the processed Key-Value tensors of static parts of a prompt, such as system instructions, so they are not recomputed for every request. When using API providers like OpenAI or Anthropic, ensuring the static content appears at the beginning of the prompt allows for cache hits that can reduce input token costs by up to 90%. However, this requires exact text matching. Semantic caching complements this by storing responses based on the meaning of queries rather than exact text matches. Using embeddings to find similar past questions can drastically reduce API calls for repetitive Q&A tasks, though it introduces engineering complexity regarding data freshness and similarity thresholds. Second, developers must minimize dormant tokens by avoiding the preloading of all available tools and context. As the number of tools grows, the static context becomes bloated, making it harder for models to select the correct function and increasing costs. Techniques like lazy-loading allow agents to fetch specific tool definitions only when needed. Similarly, maintaining a slim, stable top layer of the system prompt while keeping detailed operational data in separate files prevents the context window from filling with unnecessary noise. Third, routing and cascading models to match task difficulty can yield substantial savings. Since a large percentage of queries are simple, they do not require the most powerful and expensive models. A routing mechanism can direct simple requests to smaller, cheaper models while escalating complex tasks to larger ones. Alternatively, a cascading approach allows a cheap model to attempt a solution first, with a lightweight checker deciding whether to escalate to a stronger model if confidence is low. While this can slash costs by over 50% in some scenarios, it carries the risk of a smaller model being confidently wrong. Finally, maintaining a clean context is essential for long-term efficiency. Agents tend to accumulate junk, such as raw tool outputs, logs, and redundant file reads, which bloat the context window. By actively archiving or summarizing this data and removing irrelevant history, developers can often reduce token consumption by 30 to 70%. This approach not only lowers costs but also improves model performance by presenting a focused state. While context compression requires engineering effort, it preserves quality without the trade-offs inherent in other cost-saving measures. Ultimately, saving on tokens requires a combination of these strategies. Developers should implement prompt caching for static instructions, use semantic caching for repetitive queries, route tasks to appropriate model tiers, and rigorously prune their conversation history. By adopting these design principles, organizations can manage the rising costs of AI agents without compromising functionality.