Here’s a concise but practical overview of how large language models (LLMs) are used to “work with” operating systems, what kinds of LLMs / products exist for that purpose, architectures and integration patterns, common use-cases, and important safety/design considerations.

What “work with an OS” means

• Run as a user-level assistant that controls apps and the file system (e.g., automating shell commands, editing files, controlling UIs).
• Act as an in-OS feature (native assistant integrated by the OS vendor).
• Act as a developer tool integrated into shells, IDEs, system services, or system-management pipelines.
• Operate as an agent that can call system APIs (launch processes, read/write files, manage network connections) under controlled permissions.

Examples / classes of LLMs and products used with OS tasks

• Built-in OS assistants (vendor integrations)
  • Microsoft Copilot for Windows — LLM-powered OS-level assistant integrated into Windows UI and apps (search, settings, app actions).
  • (Other major OS vendors have explored or released LLM integrations — desktop/mobile vendors provide assistants tied to system features.)
• Agent frameworks that run on machines and interact with the OS
  • Auto-GPT / BabyAGI / Agentic approaches — autonomous agents that chain LLM calls to run shell commands, manage files, and call tools.
  • ReAct / tool-using agents — LLMs augmented with specific tools (shell, web, APIs) to accomplish tasks.
• Developer-oriented LLMs / copilots
  • GitHub Copilot / Codeium / Tabnine — assist coding and shell snippets, often integrated into IDEs and terminals.
  • ChatGPT (with local tool access) used inside terminals via community wrappers (e.g., CLI clients that call ChatGPT and then run commands).
• Integration/framework libraries
  • LangChain, LlamaIndex, Microsoft Semantic Kernel — facilitate connecting LLMs to tools, files, and system APIs.
  • OpenAI function-calling / tool specification patterns — let LLMs choose and call pre-defined system functions.
• On-device or self-hosted models
  • Local LLMs (Llama-family, Mistral, etc. ported locally) used for offline automation or privacy-sensitive tasks; typically paired with a small management layer to run system commands.

Integration patterns / architectures

• Tool invocation pattern: LLM suggests intent → system-side “tool runner” implements secure functions (run-shell, read-file, write-file) and returns structured results.
• Sandbox + mediator: LLM runs in an isolated environment; a mediator validates LLM outputs and maps them to safe OS calls.
• Prompt→Action loop (agent): LLM proposes actions (commands) → an executor runs them (optionally in a simulated or dry-run mode) → results fed back to LLM for the next step.
• Function calling API: LLM chooses named functions with typed arguments; the host executes them with enforced checks.
• Event-driven integration: OS events feed into LLM (notifications, logs); LLM suggests or executes responses.

Common use-cases

• Shell/terminal automation: generate and run sequences of shell commands, refactor scripts, perform system diagnostics.
• File and document manipulation: search, summarize, refactor or reformat files; batch rename or migrate content.
• App automation / UI scripting: automate repetitive GUI tasks via accessibility APIs (AppleScript, Windows UI Automation, xdotool).
• System administration: inspect logs, propose fixes, run diagnostic commands, apply configurations (with safeguards).
• Developer workflows: code generation, code review, test generation, committing and running build steps.
• Personal automation: calendar, emails, note-taking, macros integrating multiple apps.
• Security/forensics assistance: triage logs and alerts (requires strict controls).

Safety, security, and reliability considerations (critical)

• Principle of least privilege: grant the LLM-only the minimal capabilities required; avoid giving blanket root or admin access.
• Sandboxing & mediation: execute potentially dangerous commands in a sandbox or require human approval before destructive actions.
• Input validation & command whitelisting: validate any command or function arguments generated by the model.
• Audit logs & non-repudiation: log model decisions, invoked API calls, commands run, who authorized them, and timestamps.
• Rate limiting & fallback: limit how often the model can run privileged actions; provide deterministic fallbacks for critical operations.
• Human-in-the-loop for risky tasks: require explicit authorization for irreversible changes (deleting files, changing system-level configs).
• Model hallucination mitigation: use structured function calls, tool results, or verification steps (e.g., dry-run) to reduce hallucinated operations.
• Data privacy: avoid sending sensitive file contents to third-party LLM APIs unless allowed; prefer on-device or private-hosted models for confidential data.

Practical tips for building an LLM-OS integration

1. Define a small set of safe, typed functions the model can call (e.g., list_dir(path), read_file(path, lines), run_shell_safe(cmd, dry_run=True)).
2. Use a sandbox or dry-run mode for new or potentially dangerous operations. Show results and request confirmation before committing.
3. Keep operations idempotent where possible and maintain rollback strategies or backups.
4. Log everything and provide an easy way for administrators to audit and revoke capabilities.
5. Combine LLM natural language intent parsing with explicit tool invocation to reduce ambiguity.
6. Test on simulated environments before production—especially when the agent modifies state.

When to prefer local/self-hosted models vs cloud APIs

• Local/self-hosted: better for privacy, low-latency local control, offline usage, or regulatory constraints.
• Cloud APIs: often more capable (especially with up-to-date model capabilities), easier to maintain and scale, but require careful handling of data sent to the provider.

Quick architecture example (safe pattern)

• UI/CLI → LLM parses intent → LLM returns function call JSON (e.g., list_files, run_dry_run) → Backend executor validates + executes in sandbox → Results returned to LLM → LLM recommends next step or prompts user for confirmation → User approves → Executor runs final action with elevated but audited privileges.

Further reading / tools to explore (starter list)

• LangChain (tool orchestration and agent patterns)
• Microsoft Semantic Kernel (function calling, skill orchestration)
• OpenAI function calling and tools patterns
• Auto-GPT / ReAct papers and agent examples for sequential action planning
• OS-specific automation APIs: AppleScript/Shortcuts (macOS), Windows PowerShell + UI Automation, Linux DBus/xdotool

If you want, I can:

• Recommend a safe starter design for a specific OS (Windows/macOS/Linux).
• Provide example code for a minimal tool-invocation wrapper (typed function calling + sandboxed shell execution).
• List specific existing tools/CLI wrappers that connect ChatGPT or local LLMs to shells and files.

Which of those would you like next?