Caching, lazy-loading, routing, compaction, and more
The post Agentic AI: How to Save on Tokens appeared first on Towards Data Science.
Agentic AI: How to Save on Tokens
Towards Data Science / 4/29/2026
💬 OpinionDeveloper Stack & InfrastructureTools & Practical Usage
Key Points
- The article explains practical ways to reduce LLM usage costs specifically in agentic AI workflows.
- It highlights techniques such as caching and lazy-loading to avoid unnecessary token consumption during repeated or partial operations.
- It discusses routing strategies to send requests to the most appropriate model or path, minimizing the cost of using larger or more expensive models.
- It covers compaction and related optimizations to reduce the amount of text/context that agents must process.
- Overall, the post focuses on engineering tactics to improve both cost efficiency and system throughput in token-intensive agent systems.
