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.

Debugging Unexpected AI Agent Behavior: When Your Code Does Something You Did Not Ask For

Fazm Team·March 18, 2026·2 min read

debugging ai-agents unexpected-behavior troubleshooting development

Debugging Unexpected AI Agent Behavior: When Your Code Does Something You Did Not Ask For

You give your AI agent a clear instruction. It does something completely different - and it works. That moment of astonishment is followed immediately by a worse feeling: you do not understand why it worked.

This is one of the hardest debugging scenarios in agent development. The output is correct, but the path is opaque.

Why Agents Surprise You

AI agents operate on probabilistic reasoning, not deterministic logic. They interpret instructions through context, prior patterns, and whatever is in their current window. This means they will sometimes:

Solve the right problem using a method you did not expect
Skip steps you thought were necessary
Add steps you did not ask for that turn out to be critical
Produce correct output from what looks like wrong reasoning

The Debugging Playbook

1. Check the logs, not the output. A correct result from a broken process will break again. Read the full execution trace - tool calls, API responses, intermediate decisions.

2. Reproduce the surprise. Run the same prompt with the same context. If the behavior is inconsistent, you have a context sensitivity problem. If it is consistent, you have a prompt interpretation gap.

3. Isolate the unexpected step. Find exactly where the agent diverged from your expected path. Was it a tool choice? A parameter? A skipped instruction?

4. Ask the agent. Seriously. Add "explain your reasoning for each step" to the prompt and compare the explained path to the unexplained one.

When to Accept the Surprise

Not every unexpected behavior is a bug. Sometimes the agent found a better path. The key question is reproducibility. If it works reliably through the unexpected path, document it and move on. If it only works sometimes, you have a real problem to fix.

The goal is not to eliminate surprise - it is to understand it well enough to trust or reject it.

Debugging Unexpected AI Agent Behavior: When Your Code Does Something You Did Not Ask For

Debugging Unexpected AI Agent Behavior: When Your Code Does Something You Did Not Ask For

Why Agents Surprise You

The Debugging Playbook

When to Accept the Surprise

More on This Topic

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