Enterprise AI Analysis
The Cost of Dynamic Reasoning: Demystifying AI Agents and Test-Time Scaling from an AI Infrastructure Perspective
Authored by Jiin Kim et al. and published on 4 Jun 2025, this analysis provides critical insights into the real-world computational and sustainability challenges of deploying advanced AI agents.
Executive Impact
This paper presents the first comprehensive system-level analysis of AI agents, quantifying their resource usage, latency behavior, energy consumption, and datacenter-wide power consumption demands. Findings reveal that while agents improve accuracy with increased compute, they suffer from rapidly diminishing returns, widening latency variance, and unsustainable infrastructure costs. The study highlights the profound computational demands introduced by AI agent workflows, uncovering a looming sustainability crisis and calling for a paradigm shift toward compute-efficient reasoning.
GPU Energy Increase (Agent vs. ShareGPT)
LLM Invocations (Agent vs. CoT)
GPU Idle Time (Agent Workflow)
Throughput Gain with Prefix Caching
Deep Analysis & Enterprise Applications
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Critical Latency Bottleneck: Sequential Execution
AI agent workflows exhibit a fundamental bottleneck due to the sequential dependency between LLM inference and tool execution. GPU resources remain idle for significant portions of execution, leading to underutilization and increased overall latency. This highlights the need for system-level optimizations that can reduce serialization, such as asynchronous pipelines or speculative tool invocation.
54.5% GPU Idle During Tool ExecutionAgent Workloads: Exponential Resource Demands
AI agents, especially those using dynamic reasoning and external tools, require significantly more LLM invocations and consume substantially more input tokens per request compared to static LLMs. This leads to increased GPU compute and memory usage, driven by the accumulation of long input contexts across iterative steps.
Average LLM Invocations (Agent vs. CoT)
Average KV Cache Memory (Agent vs. CoT)
Max KV Cache Memory (Agent vs. CoT)
Enterprise Process Flow
AI agents operate through an iterative process involving LLM inference and external tool interactions. The LLM determines the next action, invokes external tools if necessary, and incorporates the observations into subsequent reasoning steps, forming a dynamic feedback loop.
LLM Inference (Reasoning/Planning)
→
Invoke External Tool
→
Observe Outcomes
→
Adapt Reasoning
Comparative Energy & Power Demands (HotpotQA)
Comparing agentic workflows (Reflexion, LATS) against conventional single-turn LLM inference (ShareGPT) reveals a dramatic increase in energy consumption and datacenter-wide power demands. Even with an 8B model, agentic systems demand gigawatt-scale power, highlighting a looming sustainability crisis.
| Model (Size) | Workflow | Accuracy (%) | Latency (s) | Energy (Wh/query) | Power (MW, @71.4M QPS/day) |
|---|---|---|---|---|---|
| Llama-3.1-8B-Instruct (8B) | ShareGPT | N/A | 4.23 (1x) | 0.32 (1x) | 1.0 M |
| Llama-3.1-8B-Instruct (8B) | Reflexion | 38 | 649.34 (153.7x) | 41.53 (130.9x) | 123.6 M |
| Llama-3.1-8B-Instruct (8B) | LATS | 80 | 380.90 (90.1x) | 22.76 (71.7x) | 67.7 M |
| Llama-3.1-70B-Instruct (70B) | ShareGPT | N/A | 6.40 (1x) | 2.55 (1x) | 7.6 M |
| Llama-3.1-70B-Instruct (70B) | Reflexion | 67 | 720.00 (112.6x) | 348.41 (136.5x) | 1.0 G |
| Llama-3.1-70B-Instruct (70B) | LATS | 82 | 305.67 (47.8x) | 158.48 (62.1x) | 471.5 M |
The Looming Sustainability Crisis of AI Agents
Unconstrained Scaling Leads to Unprecedented Power Demands
- Current daily active users (DAU) for agentic systems could drive GPU energy footprint to GWh/day, rivaling cities like Seattle.
- Scaling to search engine query volumes (13.7B daily) could push power demands to hundreds of GW, exceeding national grid capacities.
- OpenAI's Stargate cluster, projected to consume multiple gigawatts and cost $500 billion, underscores the scale of required infrastructure.
- AI agent performance does not scale proportionally with compute and energy costs, leading to diminishing returns and unsustainable burdens.
- A paradigm shift towards compute-aware reasoning and efficient inference is critical for scalable and sustainable AI agent deployment.
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Strategic Imperatives for Sustainable AI Agent Deployment
Our phased roadmap ensures your AI agent initiatives are not only powerful but also economically viable and environmentally sustainable.
Phase 1: Foundation & Optimization
Implement efficient LLM serving infrastructure with advanced caching (prefix caching) and dynamic batching. Explore architectural improvements like asynchronous pipelines or speculative tool invocation to reduce GPU idle time and improve throughput for multi-step reasoning.
Phase 2: Agent Design & Cost-Awareness
Adopt compute-aware agentic workflows, balancing accuracy with cost-efficiency. Optimize agent parameters (e.g., iteration budget, few-shot examples) to identify Pareto-optimal configurations. Implement adaptive scheduling and elastic resource allocation to manage variable latency and resource demands.
Phase 3: Parallel & Distributed Reasoning
Leverage parallel reasoning strategies (e.g., tree search with concurrent LLM calls) for latency-sensitive workloads, while carefully managing increased memory pressure. For resource-constrained environments, prioritize sequential scaling. Explore techniques for memory optimization (KV cache offloading, compression) for long input contexts.
Phase 4: Monitoring & Continuous Improvement
Establish robust monitoring for resource utilization, latency, and energy consumption. Continuously evaluate and refine agent designs and infrastructure configurations based on real-world performance data and evolving sustainability goals. Foster system-algorithm co-design.
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