A Token-efficient Data Layer for Agentic Communication

Agentic communication fundamentally differs from traditional machine communication in that its outputs are recursively consumed and interpreted by Large Language Models (LLMs). This unique characteristic makes efficient use of the model's limited context a critical requirement. However, current agent communication protocols - such as the Model Context Protocol (MCP) and Agent-to-Agent (A2A) - often suffer from token or context bloat, caused by redundant schema definitions, verbose message structures, and inefficient capability exchanges. As a result, a substantial number of tokens are unnecessarily consumed within the model’s context window. To address these issues, this draft defines the Agentic Data Optimization Layer (ADOL), a general and foundational data-layer for agent communication. ADOL introduces a set of backward-compatible optimizations, including schema deduplication through JSON references, adaptive inclusion of optional fields, controllable response verbosity, and retrieval-based selection mechanisms. Collectively, these mechanisms reduce token consumption, enhance context efficiency, and provide a scalable foundation for future agent communication frameworks.