Engineering
Cutting Token Costs 60% with Prompt Caching
Michael Rodriguez|January 28, 2026|8 min
AI agent sessions are expensive. Each action requires sending the full conversation context to the model, and sessions can run for dozens of turns. Without optimization, token costs add up fast.
The Problem
A typical agent session involves 20-50 model calls. Each call sends the system prompt, conversation history, tool definitions, and previous results. By the end of a session, you are sending thousands of tokens of repeated context with every request.
Our Solution
- ●Prompt caching stores system prompts and tool definitions across calls
- ●Conversation history is compressed at checkpoints
- ●Previous tool results are summarized rather than sent in full
- ●Static context is cached at the provider level
- ●Dynamic context is incrementally updated rather than rebuilt
Prompt caching reduced average session cost by 60% while maintaining identical task completion rates.
Making AI agents economically viable requires aggressive optimization at the infrastructure level. Prompt caching is one of the highest-impact techniques available.
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