For years, the “agent” story was mostly text → API calls → text. That works when software exposes clean endpoints, but the real world is full of:
- Legacy UIs with no API
- SaaS products where the API is incomplete or locked down
- Workflows that span apps (browser + spreadsheet + admin UI)
- Tasks where the UI is the source of truth (what’s visible, what’s enabled, what error banners appear)
Provider-native computer use tools are a response to that gap: they let a model operate software the same way a human does—by seeing the screen and performing input actions.
OpenAI frames this as a “Computer-Using Agent” capability aimed at controlling real interfaces and measuring progress on benchmarks like OSWorld (a sign they’re treating UI control as a first-class modality, not a hack) (OpenAI: Computer-Using Agent). Anthropic positions “computer use” as enabling Claude to interact with existing interfaces directly while highlighting operational safety concerns (e.g., isolate execution in a dedicated environment) (Anthropic computer use docs, Anthropic announcement).
Under the hood, the important idea is standardization:
- Providers define a tool schema (action types, fields, image formats).
- They train (and safety-tune) models to reliably emit that schema.
- They enforce constraints (environment type, context handling) that make the loop workable in production.
That’s why these tools matter: you’re not just “running Selenium with an LLM”—you’re using a model/tool pair designed together as a control system.
What “computer use” enables at a technical level
Provider computer-use is basically a minimal OS/UI control API with three properties:
1) A perception channel grounded in pixels
The model can request a screenshot and interpret UI state: text, layout, icons, highlights, banners, disabled buttons, etc. This is the “state observation” step in a control loop.
2) A constrained action vocabulary
Instead of arbitrary code execution, the model emits actions like:
- click / move / drag
- type / keypress
- scroll
- wait
- screenshot (again)
This constraint is good: fewer degrees of freedom means fewer unsafe/irreversible actions and more predictable orchestration.
3) Closed-loop autonomy
The model can iterate: observe → act → observe, handling uncertainty and recovery:
- “Did my click land?”
- “Did the UI change?”
- “Do I need to wait for the next state?”
This is what makes “computer use” different from one-shot vision: it’s not just recognition; it’s interactive control.
How your Tic-Tac-Toe project leverages these tools (and what it demonstrates)
This demo is valuable because it isolates the core computer-use loop without lots of app complexity—and still exposes the hard parts.
1) The UI becomes the “API surface”
Your agent does not get a structured board array. It must infer the board from screenshots and interact via clicks. That’s the entire point of computer-use: operate systems where the UI is the interface.
To make that reliable, the project adds an important “agent affordance”: cell labels (TOP-LEFT, CENTER, …). This is a general pattern: if you want robust UI control, you design UI elements that are easy for vision models to anchor on (stable text, consistent placement, clear state cues).
2) You turn the model into a controller, not a narrator
The implementation forces an explicit loop:
- Take screenshot
- Choose a move
- Click
- Take screenshot to verify
- Wait for opponent
- Repeat
That “verify after action” step is the difference between a demo that “usually works” and one that can recover from inevitable UI mistakes.
3) You anchor termination to UI truth (critical for reliability)
Both prompts insist the agent must only end the game when it sees the on-screen banner (“Player X wins!”, “It’s a draw!”), not when it believes it has three in a row.
This is a broadly applicable safety/reliability pattern for computer-use:
- Never end (or submit, pay, delete, send) based on internal inference alone
- Require screen evidence for critical transitions
It reduces hallucinated “success” and makes runs auditable.
4) You surface real provider constraints (OpenAI truncation, Anthropic context bloat)
Provider-native tools come with operational requirements that show up immediately in multi-step UI loops:
OpenAI: your agent sets
truncation: "auto"because OpenAI’s computer-use flow expects automatic truncation to keep long interactive sessions viable (OpenAI computer use guide). This is a concrete example of “provider tool != generic LLM call”; there are mode-specific runtime contracts.Anthropic: your agent uses middleware to clear old tool uses (screenshots). That’s essentially context garbage collection—and it’s not optional in screenshot-heavy loops. Without pruning, you hit context limits or degrade performance as stale observations pile up.
This is one of the biggest “why computer-use is hard” lessons: the environment is unstructured, and the data (images) is heavy.
5) You demonstrate why providers add more than computer control: persistent memory
The Anthropic player adds a native memory tool and stores learnings as markdown (strategy, opponent patterns, mistakes). In practice, this turns a single-session controller into something that can:
- review prior outcomes before starting
- encode opponent-specific openings
- avoid repeating mistakes across games
The demo’s memory files show exactly the value proposition: the agent loses once due to a missed threat, then blocks the same pattern next game. That’s a minimal but real example of “agent improvement” that’s hard to get from prompts alone.
Why this matters beyond Tic-Tac-Toe
This project is a good representation of where computer-use shines and where it bites:
- Shines when you need to automate UI-only workflows quickly, without building bespoke integrations.
-
Bites because reliability depends on:
- UI stability and “readability”
- verification loops
- context management
- isolation/sandboxing (providers explicitly recommend this for safety) (Anthropic computer use docs)
In other words: computer-use is best understood as a systems discipline—a control loop combining model behavior, tool constraints, UI design, and runtime safeguards.
Thanks for reading!
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