Smart-TCP: An Agentic AI-based Autonomous and Adaptive TCP Protocol

TL;DR

Smart-TCP introduces an AI-driven, autonomous TCP protocol leveraging Large Language Models to enhance reliability and adaptability, demonstrating high success rates in static prediction and error detection.

Contribution

It presents a novel agentic AI-based architecture for TCP, integrating LLMs and tool use to improve protocol autonomy and performance.

Findings

Achieved 93.33% success rate in end-to-end sessions.

Demonstrated effectiveness in static prediction and error detection.

Validated the technical feasibility of agentic AI-based TCP.

Abstract

The Transmission Control Protocol (TCP) relies on a state machine and deterministic arithmetic to ensure reliable connections. However, traditional protocol logic driven by hard-coded state machines struggles to meet the demands of intelligent and autonomous network architectures. Here, we adopt the agentic AI-based paradigm, driven by Large Language Models (LLMs), characterized by context perception, autonomous reasoning, and tool use. Based on this, we propose Smart-TCP, which re-imagines TCP's core control logic as an autonomous agent. Specifically, the proposed architecture employs a context aggregation mechanism to synthesize the protocol context, utilizes the LLM for autonomous logical reasoning, and invokes an Arithmetic Logic Unit (ALU) as a tool for computation. Furthermore, we establish a dual-agent interaction framework based on this architecture and implement TCP protocol interactions. Experiments demonstrate that the Smart-TCP agent excels in static prediction and error detection, achieving a 93.33% success rate in end-to-end sessions. These results strongly validate the technical feasibility of an agentic AI-based TCP protocol.