MyPCBench: A Benchmark for Personally Intelligent Computer-Use Agents

2026-06-15 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

MyPCBench introduces a novel benchmark designed for personally intelligent computer-use agents, addressing a critical gap in current evaluation methods that rely on impersonal environments. This benchmark simulates a real-world Linux desktop, complete with 17 web applications and a full desktop stack, all configured for a canonical persona, Michael Scott. It features 184 tasks, each derived from actual user requests. Benchmarking six models, MyPCBench found that Claude Opus 4.6 was the top performer, fully solving 55.4% of tasks and being the only model to exceed 50%. The evaluation revealed that model failures frequently occur on tasks requiring interaction across multiple applications and those involving long operational trajectories, highlighting where personalization most challenges an agent. The environment, task set, and agent harness are publicly available at https://mypcbench.com.

Key takeaway

For AI Engineers developing personal computer-use agents, you should integrate MyPCBench into your evaluation pipeline. This benchmark exposes critical weaknesses in agents' ability to handle personalized, multi-application tasks and long operational sequences, which are crucial for real-world deployment. Prioritize improving agent performance in these complex scenarios to ensure your models can effectively navigate a user's full digital life, moving beyond impersonal test environments.

Key insights

MyPCBench evaluates personal computer-use agents in a realistic, personalized desktop environment, revealing current model limitations.

Principles

• Personalization stresses agents most on multi-application, long-trajectory tasks.
• Current benchmarks lack personal context for real-world agent deployment.

Method

MyPCBench uses a Linux desktop with 17 simulated web applications and a canonical persona to define 184 real-world inspired tasks for agent evaluation.

In practice

• Use MyPCBench to test agents in personalized web environments.
• Focus agent development on multi-application task handling.

Topics

• MyPCBench
• Computer-Use Agents
• Personal Assistants
• Benchmarking
• Large Language Models
• Web Automation
• Linux Desktop

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.