AI BriefingAI Agent Latency 101: How to Make Your AI Agent Faster?
Key point
This article presents diagnostic and optimization strategies to reduce AI agent latency.
Details
Developers should focus on speed and cost optimization after developing AI agents. To reduce latency, five approaches can be utilized: diagnosing bottlenecks, improving UX, reducing the number of LLM calls, speeding up calls, and parallel processing.
The first step is to use tools like LangSmith to track the latency of each step in the agent and identify the step that contributes most to the overall latency via the 'waterfall' view.
UX strategies that lower perceived latency without actually reducing real latency are also effective. These include providing intermediate processes such as planning steps or search results via streaming, in addition to response tokens, or running the agent in the background based on events, like an email assistant, to hide latency from the user.
Not every task needs to be an LLM call. It is important to reduce unnecessary LLM calls through a hybrid approach that combines code and LLM calls. Additionally, running independent LLM calls in parallel, or applying faster models or caching techniques to speed up individual calls, also contributes to latency improvement.
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