How much time will I actually save with Fazm?

Most users report saving 45-90 minutes per day on repetitive tasks. The biggest time savings come from email management (drafting replies, sorting, archiving), form filling (expense reports, CRM updates, applications), research (comparing products, gathering data from multiple sites), and social media (cross-posting, scheduling, replying). For example, booking a flight that normally takes 10 minutes of clicking through airline sites takes a single voice command with Fazm. Filing an expense report that requires opening a PDF, extracting numbers, and entering them into a spreadsheet becomes one spoken sentence. The more repetitive and multi-step your daily work is, the more time you get back.

What exactly can Fazm control?

Fazm controls everything you can do manually on your Mac. That includes mouse movements and clicks, keyboard typing, browser navigation and form filling, app switching, file management in Finder, and system-level actions. It uses direct browser DOM control for web interactions - meaning it reads and manipulates the actual page structure rather than taking screenshots and guessing where to click. For native macOS apps, Fazm uses accessibility APIs to interact with buttons, menus, text fields, and other UI elements. This works across any application: Chrome, Safari, VS Code, Slack, Figma, Terminal, Mail, Calendar, and more.

How is Fazm different from ChatGPT or Claude?

Chatbots like ChatGPT and Claude give you answers in a conversation window. Fazm takes action directly on your computer. When you say 'reply to Sarah's email,' Fazm opens your email app, finds Sarah's message, writes a contextual reply, and sends it. A chatbot would tell you what to write, but you still have to do all the clicking and typing yourself. Fazm moves your mouse, types on your keyboard, clicks buttons, and navigates between apps. It is a computer agent, not a chat assistant.

How does the memory layer work?

Fazm builds a personal knowledge graph by extracting information from your files, browsing history, conversations, and daily activity. Over time, it learns your contacts (names, emails, relationships), accounts and credentials, preferences (formatting, tone, scheduling habits), and frequently used workflows. In your first week, you might say 'Reply to Sarah's email, she's my cofounder, her email is sarah@acme.com.' By week four, you just say 'Reply to Sarah' because Fazm already knows who she is. By week eight, Fazm can draft the reply before you even ask. All of this data stays locally on your Mac - your knowledge graph never leaves your machine or gets sent to any cloud service.

Is Fazm safe? Can it mess up my computer?

Safety is built into every layer of Fazm. You watch every action in real-time on your screen - Fazm moves your mouse and types visibly, so there is nothing hidden. You can stop any action at any point by pressing a keyboard shortcut. Fazm also shows you what it plans to do before executing destructive actions like deleting files or sending emails, giving you a chance to confirm or cancel. Because Fazm is open source, the code is fully auditable on GitHub. There is no hidden behavior, no background processes making changes you cannot see, and no actions that bypass your screen.

What platforms does Fazm support?

Fazm currently supports macOS, running natively on both Apple Silicon (M1, M2, M3, M4) and Intel Macs. It is built with Swift and uses macOS-specific frameworks like ScreenCaptureKit for screen analysis and Accessibility APIs for app control, which is why macOS is the primary platform. Windows and Linux support are on the roadmap but do not have release dates yet. You can also control your Mac remotely from your phone using Fazm Remote, which gives you voice access to your desktop while you are away from your computer.

How does Fazm voice input work?

One keyboard shortcut activates push-to-talk. Hold the key, speak naturally about what you need done, and release. Fazm transcribes your speech locally using on-device models, so there is no network latency for the voice recognition step. You do not need wake words like 'Hey Siri' or 'OK Google' - just press and talk. You can also type commands into the floating toolbar if you prefer text input. Fazm understands natural language, so you do not need to memorize specific commands. Say 'book me a flight to Tokyo next Thursday' or 'find the cheapest AirPods on Amazon' and Fazm figures out the steps.

Is my data sent to the cloud?

Fazm processes screen analysis and memory entirely on your machine. The personal knowledge graph, file indexing, and screen capture all run locally. The only data that leaves your Mac is the intent - a text description of what you want to accomplish, which is sent to the AI model (Claude or GPT) for action planning. Your actual screen content, file contents, browsing history, and personal knowledge graph never leave your computer. Fazm is also open source, so you can verify exactly what data is transmitted by reading the code on GitHub. For users who need complete air-gapped operation, local model support is planned.

Tracking AI Agent Reputation Across Multiple Dimensions

Fazm Team·March 18, 2026·3 min read

ai-agents reputation reliability observability agent-evaluation

Agent Reputation Without Context Is Just a Leaderboard

Giving an AI agent a single score - "Agent A has 94 percent reliability" - tells you almost nothing useful. Reliable at what? Under what conditions? At what cost? A reputation system that collapses everything into one number creates the same problem as a five-star review system: it is easy to game and hard to learn from.

Why Single Scores Fail

An agent might be 99 percent reliable at simple file operations but 40 percent reliable at complex multi-step workflows. Averaging these into one number makes it look mediocre at everything, when it is actually excellent at one thing and terrible at another.

Single scores also hide important tradeoffs. An agent that is 95 percent accurate but takes 10 minutes per task might be worse for your use case than one that is 85 percent accurate but finishes in 30 seconds. The score does not capture this.

Multi-Dimensional Reputation

A useful reputation system tracks dimensions separately:

Accuracy - does the agent produce correct results? Measured against ground truth when available.
Speed - how long does the agent take? Track p50, p90, and p99 latencies, not just averages.
Cost efficiency - how many tokens, API calls, or compute cycles does the agent use per task?
Failure patterns - when the agent fails, how does it fail? Gracefully with clear errors, or silently with wrong outputs?
Recovery - after a failure, does the agent recover on retry or does it repeat the same mistake?

Context-Dependent Scoring

The same agent performs differently depending on context. Track reputation per task type, per time of day (API rate limits vary), per input complexity, and per dependency chain. An agent that works perfectly as a standalone might degrade when it depends on three other agents' outputs.

Making It Actionable

Reputation data is only useful if it drives decisions. Use it for:

Task routing - send complex tasks to agents with high accuracy, send simple tasks to fast agents
Capacity planning - agents with declining scores might need updated prompts or more resources
Trust boundaries - agents with low accuracy on certain tasks should not be assigned those tasks unsupervised

Track the dimensions that matter for your system. Not all of them will be relevant, but a single number never is.

Tracking AI Agent Reputation Across Multiple Dimensions

Agent Reputation Without Context Is Just a Leaderboard

Why Single Scores Fail

Multi-Dimensional Reputation

Context-Dependent Scoring

Making It Actionable

More on This Topic

Related Posts

Why Your AI Agent Should Never Depend on a Single LLM Provider

Data Quality as a Moral Imperative for AI Agent Analytics

How to Monitor AI Agent Health in Production