Since 2022, systems built on large language models have moved from "generate a paragraph" to autonomous agents. In production, an AI agent usually wraps an LLM with tool calls, state, and orchestration. It is an autonomous module that combines three capabilities: planning (breaking a goal into steps), memory (short-term in context, long-term in external stores), and execution (calling APIs, writing files, triggering other services).
In production deployments, teams run into interface fragmentation. The model lives in one place, the tools in another, other agents elsewhere, and the corporate security perimeter somewhere else entirely. All of these must be wired together. The classic formulation of this pain is the N×M problem: connecting N clients to M data sources requires writing N×M unique adapters. Five models and ten tools turn into fifty hand-coded connectors that nobody wants to maintain.
Vendors and open-source projects are proposing several protocol families. Several protocols now target different parts of agent integration — specifications that each take over a specific piece of the wiring: how the model reaches a tool, how an agent lives in time, how two agents negotiate, how data safely crosses the perimeter. Below is a breakdown of ten such protocols and standards, grouped by architectural layer.
Continue reading “The AI Agent Protocol Stack: A Map of the Layers”