Technical Summary: AgentStop

Problem Statement

The paper addresses the growing resource inefficiency of deploying Large Language Model (LLM) based autonomous agents on consumer devices. While local deployment offers significant advantages in privacy and cost by eliminating cloud API fees and data transmission, it introduces severe constraints on device battery life and thermal management. Unlike single-turn LLM interactions, agentic workflows involve iterative reasoning, tool use, and failure retries, which can lead to thousands of tokens being generated and significant compute time spent on trajectories that ultimately fail to complete the task. The authors note that these failed runs consume substantial energy without providing utility, potentially draining a significant portion of a laptop's battery (e.g., 3–7% for a single failed complex task) and causing thermal stress (GPU temperatures reaching ~95°C). Existing efficiency metrics often rely on token counts or latency, which fail to capture the actual energy cost and hardware utilization of these sustained, multi-step processes.

Methodology

The authors propose AgentStop, a lightweight, ML-based efficiency supervisor designed to predict and preemptively terminate agent trajectories that are unlikely to succeed.

• Data Collection: The system collects execution traces from agents running on benchmarks (web-based QA and coding tasks). These traces include generated tokens, their associated log probabilities (logprobs), tool call outputs, and final success/failure labels.
• Feature Engineering: AgentStop extracts low-cost signals directly available during standard inference, avoiding the need for additional heavy computation. Key features include:
  • The top $k$ smallest output log probabilities at each agent step (indicating model uncertainty).
  • The number of output tokens per step (chain-of-thought length).
  • The ratio of token overlap between adjacent steps (detecting repetitive loops).
• Model Architecture: The supervisor is implemented as a Gradient-Boosted Decision Tree (GBDT) model (specifically XGBoost). The authors chose GBDT over deep learning models due to its low inference latency, minimal energy footprint (<0.01 mWh per inference), and strong performance on structured tabular data.
• Training and Deployment: The model is trained to classify whether an agent trajectory will eventually succeed or fail. It is deployed as a "supervisor" that checks the agent's state after specific steps. If the predicted probability of success falls below a calibrated threshold, the agent is terminated early.

Key Contributions

1. Empirical Characterization of Agent Energy Costs: The paper provides the first detailed measurements of time, token, and energy overhead for locally deployed agents on consumer hardware (Apple M1 Max). It demonstrates that failed agent runs consume significantly more energy than successful ones and that GPU power draw and temperature spike during these inefficient loops.
2. AgentStop Framework: The introduction of a practical, lightweight mechanism to predict agent failure using signals already present in the inference stream (logprobs). This approach requires no modification to the underlying LLM and introduces negligible overhead.
3. Efficiency-Utility Trade-off Analysis: The work establishes that predictive early termination can significantly reduce wasted energy with minimal impact on task success rates, reframing efficiency supervision as a critical enabler for sustainable on-device AI.

Experimental Results

The authors evaluated AgentStop on two benchmarks:

• Web-based Question Answering: Using the FRAMES and SimpleQA datasets with Qwen3-30B-A3B and Qwen3-1.7B models.
• Coding: Using the SWE-Bench Verified dataset with Qwen3-Coder-30B.

Key Findings:

• Energy Savings: AgentStop reduced energy wastage by 15–20% on challenging benchmarks. For example, on the FRAMES dataset, it achieved over 20% energy reduction by step 5 with less than a 5% drop in task utility.
• Performance: The system achieved an AUC-ROC of 0.6–0.7 within the first few steps of execution, indicating that failure signals are detectable early.
• Comparison: AgentStop outperformed baseline strategies such as random stopping, stopping based on minimum log probability, or stopping based on mean log probability.
• Model Dependency: The supervisor was effective for the larger Qwen3-30B-A3B model, which exhibited clearer confidence signals (logprobs) in failed runs compared to the smaller 1.7B model.

Significance and Claims

The paper claims that AgentStop provides a practical mechanism for enabling sustainable, privacy-preserving LLM agents on user devices. By mitigating unnecessary battery drain and execution time, predictive early termination addresses critical human-centered concerns such as device longevity, usability, and the anxiety associated with low battery levels (nomophobia). The authors argue that efficiency supervision is not merely an optimization but a necessary component for the real-world adoption of local agents, allowing them to operate without compromising the user's device availability or thermal comfort. The work positions early termination as a viable strategy to bridge the gap between the high resource demands of agentic workflows and the limited energy budgets of consumer hardware.