The Agent War Is About Approach, Not Just Models

Every major AI company now has some form of computer-use agent. But the differences aren't in the underlying language models - they're in the architecture decisions about how the agent interacts with your machine.

Screenshot-Based vs Accessibility-Based

Manus and Claude CoWork primarily use screenshot analysis. The agent takes a picture of your screen, interprets what it sees with a vision model, and decides where to click. This works across any application but is inherently slow - each action requires capturing a frame, sending it for analysis, and waiting for coordinates.

OpenClaw uses the accessibility tree - the structured representation of UI elements that screen readers rely on. Instead of interpreting pixels, it reads the actual button labels, text fields, and menu items. Faster and more reliable, but limited to applications that properly expose their accessibility data.

Cloud vs Local Execution

Manus runs your tasks in a cloud VM. Your session is isolated but remote. CoWork shares your screen via cloud infrastructure. Both require internet and send data off your machine.

Local agents run directly on your Mac. The LLM can run on-device using Apple Silicon, or use an API for heavier reasoning while keeping all screen data and actions local. The privacy profile is fundamentally different.

Memory and Context

This is where everyone struggles. Manus creates a new VM per session. CoWork has limited session memory. OpenClaw maintains basic state. None of them build a persistent knowledge graph that grows over time and remembers your preferences, projects, and patterns.

The Winner Will Combine All Three

The agent that wins this war will be local-first for privacy and speed, accessibility-based for reliable control, and memory-rich for personalization. It's a hard combination to build, but each piece exists. The challenge is integration.

Fazm is an open source macOS AI agent. Open source on GitHub.