What Limits Agentic Systems Efficiency?
Authors: name: Song Bian, name: Minghao Yan, name: Anand Jayarajan, name: Gennady Pekhimenko, name: Shivaram Venkataraman
Organization: University of Wisconsin - Madison

Large Language Models (LLMs), such as OpenAI-o1 and DeepSeek-R1, have
demonstrated strong reasoning capabilities. To further enhance LLM
capabilities, recent agentic systems, such as Deep Research, incorporate web
interactions into LLM reasoning to mitigate uncertainties and reduce potential
errors. However, existing research predominantly focuses on reasoning
performance, often neglecting the efficiency of agentic systems. In this work,
we present a comprehensive empirical study that identifies efficiency
bottlenecks in web-interactive agentic systems. We decompose end-to-end latency
into two primary components: LLM API latency and web environment latency. We
conduct a comprehensive empirical study across 15 models and 5 providers to
demonstrate high variability in API-based agentic systems. We observe that web
environment latency can contribute as much as 53.7% to the overall latency in a
web-based agentic system. To improve latency, we propose SpecCache, a caching
framework augmented with speculative execution that can reduce web environment
overhead. Extensive evaluations on two standard benchmarks show that our
approach improves the cache hit rate by up to 58x compared to a random caching
strategy, while reducing web environment overhead by up to 3.2x, without
degrading agentic system performance.