Practical strategies for reducing AI agent latency: identify bottlenecks with observability tools like LangSmith, improve perceived latency through streaming and background execution, reduce the number of LLM calls by combining code with LLM calls (e.g. via LangGraph instead of general multi-agent frameworks), speed up individual calls with faster models or less context, and parallelize LLM calls where possible.
Table of contents
Identifying where the latency is coming fromChanging the UX to reduce the “perceived” latencyMaking fewer LLM callsSpeeding up LLM callsMaking LLM calls in parallelConclusionQuestions this post answers
How can I reduce latency in my AI agent application?
Start by identifying where latency actually comes from using an observability tool, since the fix depends on whether it's one large LLM call or many small ones adding up. Then reduce perceived latency by streaming intermediate steps, make fewer LLM calls by combining code with LLM calls instead of general multi-agent architectures, use faster models or shorter context, and parallelize calls where independent. Developers tuning agent performance follow practical latency techniques like these on daily.dev.
Why do multi-agent supervisor or swarm architectures feel slow and expensive?
Multi-agent supervisor and swarm setups use a large number of LLM calls because they are general-purpose architectures not optimized for a specific use case, leading to inefficient communication between agents. A common progression is moving from a single LLM call to a ReAct agent to a multi-agent setup, then to a lower-level framework like LangGraph that explicitly defines how agents communicate to cut down on calls. Teams weighing agent architecture tradeoffs track patterns like this on daily.dev.
Does streaming intermediate agent steps actually reduce perceived latency?
Yes, showing intermediate steps such as retrieval results or a plan improves user satisfaction even when total completion time stays the same. Perplexity's search interface is cited as an example where surfacing these in-progress steps in the UI made users perceive the agent as faster, despite no actual reduction in response time. Developers refining agent UX weigh perceived versus actual latency using resources on daily.dev.