The Part of AI Coding Agents I Didn't Expect to Care About: Latency
I spent the last few months pretty much living in Claude Code. It became a regular part of how I worked: give it a task, let it work through the codebase, review what it changed, and keep iterating. Today I ran the same multi-step agentic task through Google Antigravity, and the difference surprised me. Antigravity, running Gemini Flash 3.8, finished the task in about half the time Claude Code…
While testing the capabilities of different AI coding agents, I was surprised by how much the speed of operation mattered, beyond just how well the model performed. One agent, Google Antigravity running Gemini Flash 3.8, completed a multi-step task in roughly half the time it took Claude Code with Sonnet 5. The improved speed was indeed pleasant, but what truly stood out was the impact of latency on productivity.
When using an AI coding agent that requires waiting for steps to complete, the human often finds themselves distracted during those idle moments. Checking messages, browsing tabs, or simply thinking about unrelated topics becomes common. Even after the agent finishes, the developer has to spend additional time reorienting themselves to the problem and figuring out where they left off.
This context switch can be surprisingly costly, not just in terms of the time spent waiting, but also in the mental effort required to regain focus and maintain the problem's context.
However, when the agent operates with low latency, the experience is markedly different. The rapid feedback loop allows developers to quickly review the agent's changes, understand the implications, and issue new instructions. This seamless, second-by-second interaction makes it easier to stay focused on the task at hand. The developer's mental model remains intact, and they can continue building on previous steps without losing momentum.
While benchmarking AI coding agents typically focuses on factors like code quality, reasoning abilities, and tool call accuracy, this experience highlights another crucial metric: the time it takes to get back into the interaction loop. The latency of the system, in essence, affects productivity in ways that raw performance metrics may not fully capture.
This shift in perspective underscores that the ultimate goal of agentic development should not only be about maximizing the model's capabilities but also ensuring that the system effectively keeps the human developer in the loop. After all, the most significant productivity gains may come from minimizing distractions rather than merely increasing the agent's processing speed.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
