A plugin named Caveman can reduce AI agent token costs by 75% by stripping away redundant communication and optimizing context space. It teaches agents to be efficient communicators, focusing on essential information for developers.
This article proposes that the misalignment between users and AI coding agents is a UI problem, not a model problem, suggesting agents should emit HTML instead of Markdown. It introduces using screenshots for context compression, inspired by DeepSeek-OCR, to enable HTML use at every plan stage without increasing token costs.
An experiment revealed an AI agent consumed 26,000 tokens and wasted $0.40-0.60 by repeatedly performing the same browser search task 10 times. The agent failed to learn from previous sessions, treating each execution as a new exploration phase and demonstrating inefficiency in repetitive operations.
This article explores the parallelization of AI agents, identifying which approaches are effective and which lead to high token consumption. It investigates the reasons behind these outcomes, aiming to optimize efficiency and cost.
The article introduces PAX Protocol, a structured and compressed communication format developed for multi-AI agent systems. Its aim is to cut token costs by approximately 70% and maintain context among 13 agents that need to communicate constantly, replacing expensive English prose.
This API optimizes AI prompts by analyzing them for token efficiency, clarity, and effectiveness, returning improved, token-saving versions. It offers various endpoints for optimization, analysis, and token estimation, with both free and paid usage plans.