@pallavishekhar_: How to reduce token usage in AI Agents? Let's understand. AI Agents use LLMs to think, plan, and recommend tools. Every…

How to reduce token usage in AI Agents? Let's understand. AI Agents use LLMs to think, plan, and recommend tools. Every step sends tokens in and gets tokens out. So, we must reduce token usage wherever we can. The first one is Prompt Caching. Most agent calls repeat the same system prompt and tool definitions. We cache this static part once. The model reuses the cached tokens instead of reprocessing them on every turn. Next comes Context Summarization. As the conversation grows, the history becomes long. We compress older messages into a short summary. The agent keeps recent messages in full and the summary for older context. This way, we do not carry the full history forever. Now, smaller models for simple tasks. Not every step needs the biggest model. For classification, routing, or simple summarization, a smaller model is enough. We use big models only for complex reasoning. We must also trim the tool results. Tools often return huge outputs - long JSON, long web pages, big files. We trim them before passing back to the model. Keep only what matters for the next step. Subagents help a lot. Instead of stuffing everything into one context, we spawn subagents for sub-tasks. Each subagent runs in its own context. Only the final result comes back to the main agent. The main context stays clean. For knowledge access, use RAG. Instead of pasting full documents, it fetches only the relevant chunks. We pass small, targeted context, not the whole library. Finally, keep the system prompt tight. Every extra word costs us on every single turn. It adds up across thousands of calls. This is how we reduce token usage in AI Agents.