Benchmarking NLIP and A2A: Measuring the Cost of an Agent Communication Hop

Summary

Autonomous agents built on large language models need protocols that let systems interoperate, but the performance of those protocols has been insufficiently measured. This study compares the Natural Language Interaction Protocol (NLIP) with Agent-to-Agent (A2A), breaking latency into message creation, connection, and send phases across three independent hardware environments. For lightweight coordination, NLIP is 8.4 to 9.6 times faster than the baseline A2A SDK on two environments and about four times faster on a third, although the size of the advantage depends on the hardware. The protocols are close to parity in the full end-to-end pipeline because LLM inference dominates that workload. The measured difference in lightweight coordination comes almost entirely from connection setup. Enabling connection caching in the A2A SDK reduces the gap from 2.75 times on one machine to near parity on faster hardware; at scale, cache-optimized A2A matches NLIP in that setting. NLIP is also about four times faster than the more optimized Python-A2A on the same stage. The authors report the residual send-phase cost without claiming a single causal explanation and conclude with a protocol-selection guide based on workload characteristics.