Performance and Energy Implications of Designing Efficient Agentic Workflows: Pitfalls and Opportunities

Agentic systems extend traditional LLM inference by coordinating specialized agents with external tools and data sources to execute increasingly complex, multi-step tasks. However, translating these capabilities into cost-effective real-world deployments remains an open challenge. Agentic design choices (including call frequency, tool integration, and orchestration) can substantially change latency and energy while maintaining similar accuracy. In our evaluation, iso-accurate configurations differ by as much as 238s and 1,013x in latency, or 177.8kJ and 1,153x in energy per task. In this paper, we construct 1,656 agentic systems across a diverse set of challenging, state-of-the-art agentic benchmarks and characterize the performance and energy implications of key agentic design decisions from a systems perspective. Through this characterization, we identify opportunities to improve the efficiency of agentic systems that can be used to guide more efficient deployments.