AI agent latency is a common challenge for developers. Initial efforts often focus on getting the agent to function, followed by an examination of speed and cost. The first step is to pinpoint the source of latency, distinguishing between large LLM calls and multiple smaller ones. Tools like LangSmith provide complete visibility into agent interactions, including latency tracking for each step, and a ‘waterfall’ view to highlight contributing stages.
Streaming responses is a common technique to improve perceived latency. By continuously delivering results, the agent appears more responsive, as demonstrated by Perplexity’s UI. Another approach is to run agents in the background, hiding latency from the user, as exemplified by an email assistant. Developers can also reduce latency by minimizing the amount of input passed to LLMs, recognizing that response time is proportional to input length. Frameworks like LangGraph offer full control over prompts, avoiding hidden complexities.
Faster models, such as Google’s Gemini Flash, and smaller, faster models from OpenAI and Anthropic, can significantly reduce latency. However, these models often trade off accuracy for speed. Parallelism, supported natively by LangGraph, allows for concurrent execution of tasks like guardrail checks or document extraction, further enhancing speed. Strategic tradeoffs between performance, cost, and capability are key to optimizing agent latency.
LangSmith and LangGraph provide tools to debug agent decisions, evaluate changes, and deploy agents in one click. The ability to manage agent deployments and monitor performance is critical for production environments. Source: https://www.langchain.com/blog/how-do-i-speed-up-my-agent
