Build AICore as a control layer, not as a model with direct access to operating-system APIs. Give it a stable contract for observations and actions, delegate execution to platform-specific adapters, check every action against policy, then capture a fresh observation to verify what changed. The available sources do not identify a canonical AICore package or specification; this is a proposed architecture for an agent that can control a computer.
What AICore should be responsible for
AICore sits between an agent or planner and the environment it controls. Its job is to make the interaction predictable across backends without pretending that Windows, macOS, Linux, and browser interfaces expose identical controls.
- Normalize: represent observations and requested actions with a stable, typed contract.
- Preserve differences: retain platform-specific properties that a shared model cannot express.
- Guard: validate actions, check authorization and safety policy, and request confirmation where required.
- Dispatch: route approved actions to the adapter that can execute them.
- Verify: observe the interface again and report whether the intended state was reached.
This boundary lets an agent reason about a common interface while leaving low-level interaction to code that understands the target platform. It does not eliminate backend-specific work: accessibility coverage, native action support, and error behavior still vary by environment.
Define the observation and action contract first
An observation should carry enough context to interpret an action safely and later determine whether it succeeded. At minimum, include the target window or surface identity, viewport dimensions, capture time, backend identity and version, and either a semantic tree, a screenshot, or both. Give each observation a unique identifier or sequence number so an action can be tied to the state on which it was based.
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For a semantic tree, represent nodes with stable identifiers for that observation, roles, names, states, bounds, supported actions, and any native properties needed by a backend. A screenshot-based observation should also identify its coordinate space and dimensions. Avoid flattening useful platform details into generic fields that lose meaning.
Use a typed action vocabulary rather than passing free-form instructions to an adapter. A starting set might include click, type, scroll, keypress, focus, set value, wait, and semantic actions such as activating a node. Validate each action’s parameters before it leaves the control layer.
struct Observation {
id: ObservationId,
target: TargetId,
captured_at: Timestamp,
viewport: Viewport,
backend: BackendInfo,
content: ObservationContent, // semantic tree, screenshot, or both
}
enum Action {
Click { target: ActionTarget },
TypeText { text: String },
Scroll { target: ActionTarget, delta: ScrollDelta },
Keypress { keys: Vec<Key> },
Focus { target: ActionTarget },
SetValue { target: ActionTarget, value: String },
Wait { duration: Duration },
}
struct ProposedAction {
based_on: ObservationId,
action: Action,
}This sketch defines a design boundary, not a drop-in cross-platform library. In particular, a real implementation needs concrete platform types, serialization rules, error types, and backend-specific behavior.
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Choose semantic, visual, or hybrid control per task
Accessibility-backed actions and screenshot/coordinate actions solve overlapping but different problems. The cited material does not establish a universal accuracy, reliability, or latency winner, so select and evaluate them against the interface you need to control.
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|---|---|---|---|
| Semantic accessibility control | Structured roles, names, states, bounds, and element actions exposed by the target. | Platform coverage; completeness of the exposed tree; which actions are supported; whether native properties preserve needed detail. | Prefer it when the target exposes usable structure and actions. Keep a path for cases where the tree is incomplete or an action is unavailable. |
| Screenshot and coordinate control | A visual capture plus coordinates interpreted relative to a known viewport. | Dependence on viewport geometry; support for unstructured interfaces; need for repeated captures; recovery after a misclick. | Keep it available for interfaces without actionable structure, and bind coordinates to the exact observation and viewport that produced them. |
| Hybrid control | Semantic data where useful, with visual observation or coordinate actions for gaps. | Fallback rules; added implementation complexity; whether each result can be independently checked. | Define explicit fallback conditions and verify outcomes after either kind of action instead of silently switching methods. |
The Computer Use Protocol (CUP) repository is one candidate reference for normalization. It describes differences among Windows UI Automation, macOS AXUIElement, Linux AT-SPI2, and web ARIA, and proposes roles, states, canonical actions, and a place for raw native properties under node.platform.*. CUP describes 15 canonical action verbs, but its repository is a project proposal, not a formal platform standard. Review its status and implementation before adopting its schema.
The repository also advertises compact-format efficiency claims of “~15x fewer tokens than the next closest format” and “~97% token reduction.” The opened material does not provide enough benchmark methodology to treat either figure as an independently verified result; they should not guide a design decision without reproducible evidence.
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Keep the planner separate from platform execution
The planner should propose an action against a particular observation. AICore should decide whether that proposal is well-formed, current, authorized, and safe before an adapter sees it. The adapter then translates it into native API calls or browser automation and returns an explicit outcome, including native errors where available.
- Receive a proposal: require the action to name the observation it uses as its basis.
- Validate the reference: reject an unknown or stale observation, a missing target, or parameters that do not fit the action type.
- Check target bounds: for coordinate actions, ensure the point lies within the viewport recorded for that observation and is routed to the intended window. For semantic actions, ensure the target belongs to the current tree.
- Apply policy: allow, require confirmation, or block the operation according to user authorization and configured rules.
- Dispatch through the matching adapter: return success, failure, or a specific error; do not report an action as successful merely because a call was issued.
For example, Google’s Computer Use documentation shows a client-side flow in which the model receives screenshots, returns function calls, the client executes those calls, and the updated screenshots are sent back. Its example uses Playwright as a browser-side handler; that example does not establish Playwright as a controller for every native desktop environment. See Google’s Computer Use documentation.
Close the loop with a fresh observation
Computer control is a repeated observe–propose–validate–execute–check cycle, not a one-time command. After execution, capture a new observation, associate it with the action and its result, and ask whether the requested state is now visible. If it is not, the agent should re-plan from the new state, report a recoverable failure, or stop according to policy.
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- Do not reuse old coordinates after a viewport change or a new capture that changes the target layout.
- Distinguish an adapter-level result (for example, a native call returned without an error) from task-level success (the intended UI state appeared).
- Set explicit stop conditions, such as task completion, user interruption, a blocked action, repeated failure, or an uncertainty threshold chosen by the application.
- Keep a user-visible stop control available while the agent is acting.
Correlation matters for diagnosis: retain the observation identifier, proposed action, policy decision, adapter result, and resulting observation as one sequence. Minimize sensitive data in logs and apply appropriate retention controls.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Make safety policy part of the execution path
Policy checks must happen before dispatch, not only in a prompt to the model. Google’s Computer Use documentation describes actions that may be allowed, require confirmation, or be blocked. A client should halt on a blocked action and obtain confirmation when the policy requires it. Google recommends using a sandboxed virtual machine or container for execution and warns against unsupervised use for critical decisions, sensitive data, or actions whose serious errors cannot be corrected.
The documentation states: “As a Preview capability, Computer Use may contain errors and security vulnerabilities.” Treat that warning as a reason to constrain permissions, isolate the environment where appropriate, and make stop and recovery paths explicit—not as a substitute for your own threat model.
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- Give adapters only the permissions needed for the task and environment.
- Require confirmation for consequential actions as defined by the application’s policy; block actions the user has not authorized.
- On policy uncertainty or adapter failure, stop or ask for guidance instead of silently retrying a potentially consequential action.
- Make the user’s authorization scope and cancellation mechanism visible and enforceable.
Safety details and API behavior can change. Consult the Computer Use API documentation for the provider’s current guidance before integrating it.
Use Rust agent projects as references, not as desktop backends
Rust libraries can inform orchestration and feedback-loop design, but the cited projects do not establish a complete, universal desktop-control stack.
| Project | Scope documented in its Rust docs | What it can inform | Boundary to keep in mind |
|---|---|---|---|
| car_ui_agent (the opened docs page displayed version 0.23.0) | An in-process adaptive A2UI rendering improvement loop: it consumes renderer RenderReport telemetry and returns a Decision that the caller routes through a surface store. |
A callback shape in which telemetry informs a decision and the caller applies it. | Adaptive rendering decisions are not evidence of desktop observation or operating-system input support. |
| ADK-Rust (the opened docs page displayed version 2.2.0) | A broader modular agent framework documenting agents, tools, sessions, workflows, browser automation, guardrails, observability, and other feature-gated services. | Agent orchestration and organization of tools, sessions, workflows, and guardrails. | The reviewed documentation does not establish a universal accessibility backend for operating systems. |
These displayed crate versions describe the opened documentation pages, not a guarantee about the latest release or available features. Check the current crate documentation and feature flags before choosing a dependency.
Test adapters and recovery, not just action dispatch
A control layer should be evaluated with each backend and target interface it claims to support. Do not infer cross-platform behavior from a browser example or a protocol schema. Build tests around the contract and failure boundaries:
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- Stale action rejection: confirm an action tied to an old observation cannot target a changed tree or viewport without a new observation.
- Bounds and type validation: exercise out-of-bounds coordinates, missing semantic targets, malformed parameters, and unsupported actions.
- Policy behavior: verify allowed actions proceed, confirmation-required actions pause, and blocked actions never reach an adapter.
- Failure reporting: simulate native errors, timeouts, and partially completed actions; confirm the result is not mislabeled as success.
- Outcome verification: test that a fresh observation is captured and the controller stops or re-plans when the desired state is absent.
- Interruption and logging: test user cancellation and ensure operational logs avoid retaining unnecessary sensitive screen or text data.
No comparative accuracy, latency, reliability, or adoption figure is established by the cited material. Choose semantic, visual, or hybrid control based on measured behavior in the environments you support, and report the test conditions rather than presenting a universal winner.
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