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What “AICore” should mean
The available sources do not identify a canonical package or specification called AICore. Here, AICore is the name for an architecture you build: a shared contract between an agent and computer-control backends. The contract should make common operations portable without pretending that Windows, macOS, Linux, and browsers expose identical interfaces.
Keep the agent-facing layer separate from the code that calls operating-system or browser APIs. The agent proposes an action against an observation; the control layer checks that proposal against current state and policy; an adapter performs the operation and reports what happened. That separation makes it possible to change an adapter without teaching the planner every platform’s native API.
Define the contract before choosing backends
Represent observations with both normalized and native detail
An observation should give the planner enough context to refer to what it sees, and give the adapter enough context to act safely. Include a unique observation identifier, capture time, target window or surface identity, viewport dimensions, and backend metadata. For semantic observations, include elements with stable identifiers for that observation, roles, names or labels, states, bounds, and supported actions. For visual observations, include the screenshot or its reference and the coordinate space it uses.
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Do not throw away platform-specific properties during normalization. A normalized role such as “button” helps a planner reason consistently, but native attributes may be essential to interpret or operate a particular control. One candidate design reference, the Computer Use Protocol (CUP) repository, describes platform representations including Windows UI Automation, macOS AXUIElement, Linux AT-SPI2, and web ARIA. It proposes common roles, states, and canonical actions while preserving raw native properties under node.platform.*. CUP is a project proposal, not a formal platform standard; review its implementation and status before adopting its schema.
Make actions typed and observation-bound
Use an explicit action vocabulary rather than asking the model to emit arbitrary operating-system commands. Common actions include click, type, scroll, keypress, focus, set value, and wait, plus semantic actions supported by a particular element. Each action should identify the observation it was based on and its target: an element identifier for semantic control, or a point in a declared coordinate system for visual control. Validate required parameters, bounds, text limits, and supported action types before dispatch.
A small conceptual Rust contract might look like this; it illustrates the boundary, not a drop-in crate API:
Rank #2
struct ObservationId(String);
struct Observation {
id: ObservationId,
captured_at: Timestamp,
surface: SurfaceInfo,
content: ObservedContent,
backend: BackendMetadata,
}
enum ObservedContent {
AccessibilityTree(Vec<Element>),
Screenshot(ImageRef),
Hybrid {
elements: Vec<Element>,
image: ImageRef,
},
}
enum Action {
Click { target: Target },
TypeText { target: Target, text: String },
Scroll { target: Target, delta: ScrollDelta },
Keypress { target: Target, key: Key },
Focus { target: Target },
SetValue { target: Target, value: String },
Wait { duration: Duration },
}In production, use concrete types for timestamps, geometry, keys, text-entry rules, and errors rather than leaving those concepts implicit. An action result should distinguish execution success from goal success: an adapter may successfully send a click even though the interface did not reach the intended state.
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Choose semantic, visual, or hybrid control per surface
Accessibility-backed actions and screenshot-coordinate actions are complementary. The right observation path depends on the target interface and what it exposes; the cited sources do not establish a universal reliability ranking or a fallback policy that works for every application.
| Approach | What it provides | What to evaluate |
|---|---|---|
| Semantic accessibility control | Structured roles, names, states, bounds, and element actions when exposed by the target. | Whether the tree is complete and current, which actions are supported, and whether native properties needed for the task survive normalization. Platform adapters must account for differing accessibility APIs. |
| Screenshot and coordinates | A visual representation and locations in a defined viewport, including for interfaces with little usable semantic structure. | Dependence on viewport geometry, whether coordinates are still valid after layout changes, and how the system detects and recovers from a misclick. This path generally needs a fresh visual observation to check the result. |
| Hybrid | Semantic elements and a visual view in one observation, allowing the planner or policy layer to use both kinds of evidence. | Extra capture and normalization work, how element bounds align with image coordinates, and which method to try when one representation lacks the needed information. |
Measure these trade-offs on the applications and tasks you intend to support. The sources reviewed do not provide an independent comparative study of accuracy, latency, or reliability, so do not present one approach as a benchmark winner.
Rank #3
Build the control loop around verified state changes
Computer control is not a one-shot command. Google’s documented Computer Use flow sends the model a goal and screenshot, receives a proposed function call, executes that call on the client, and returns a new screenshot so the model can continue. A Rust layer can use the same basic loop while allowing semantic observations and other backends.
- Capture: Ask the selected adapter for a fresh observation of the authorized surface. Record its identifier, timestamp, window identity, and geometry.
- Plan: Give the model or planner the user’s goal, relevant policy, and current observation. Require a typed proposed action, not direct access to system APIs.
- Validate: Confirm the action refers to the current observation, targets an existing element or valid coordinate, fits the viewport where applicable, is supported by the adapter, and is permitted by policy.
- Authorize: Block prohibited actions and pause for explicit confirmation when policy requires it. Do not let a planner override these decisions.
- Execute: Dispatch the approved action through the adapter. Return an explicit result, including native errors or a reason execution was rejected.
- Verify: Capture a fresh observation and compare it with the intended state. If the result is uncertain or the goal was not reached, re-plan from the new state rather than claiming success.
- Stop: End on verified goal completion, user interruption, a policy block, a configured limit, or an unrecoverable adapter failure.
Google’s example uses Playwright as one browser-side action handler; that example does not establish Playwright as a controller for every native desktop environment. Keep browser automation and native desktop control behind interfaces that report their different capabilities and errors.
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Keep planning separate from platform execution
Use adapter boundaries, not one giant platform-specific agent
Define observation and execution interfaces in the core, then implement adapters for the environments you support. A browser adapter might expose DOM- or accessibility-derived elements and browser actions; native adapters may call the relevant platform accessibility or input APIs. Report capability differences explicitly—for example, whether an adapter can expose element bounds, set a control’s value, capture a screenshot, or perform a semantic action.
Keep raw platform handles inside the adapter. The planner should receive serializable identifiers tied to an observation, not pointers or handles that can outlive the state from which they came. The adapter can resolve an identifier during validation and reject it if the underlying target has disappeared or changed.
Return meaningful outcomes
Model adapter results so the caller can distinguish a rejected action, an API error, a timeout, and an action that was dispatched but whose effect is not yet known. Include native error context for logs and debugging, but avoid exposing secrets or unnecessary personal data to the model. Never convert “input was sent” into “the task succeeded” without observing the resulting state.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Put policy and recovery in the execution path
Safety checks belong between the planner and the adapter, not only in the prompt. Google’s Computer Use documentation describes actions as allowed, confirmation-required, or blocked, recommends using a sandboxed VM or container, and warns that the preview capability may make errors. Its exact warning is: “As a Preview capability, Computer Use may contain errors and security vulnerabilities.” See the Google AI for Developers Computer Use documentation for that product’s guidance; its API behavior and availability may change.
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- Confirmation required: Pause and ask the user before execution; resume only after receiving an affirmative decision tied to the pending action.
- Blocked: Do not dispatch. Return a clear reason and stop or ask the planner for a different permissible course only when that is safe.
Provide an accessible, immediate user stop control. Bound action counts and execution time, handle timeouts and stale observations, and log decisions and outcomes with sensitive content minimized. Do not run unsupervised computer-use agents for critical decisions, sensitive data, or actions where a serious error cannot be corrected; Google’s documentation specifically cautions against those uses.
Use Rust agent libraries for the part they document
Existing Rust projects can inform orchestration and feedback-loop design, but the cited documentation does not establish a finished, universal desktop-control adapter.
- car_ui_agent documentation describes an in-process UI-improvement agent for an adaptive A2UI rendering loop. It consumes renderer
RenderReporttelemetry and returns aDecisionfor the caller to route through a surface store. The latest page reviewed displayed crate version 0.23.0. This is an example of telemetry-driven decisions, not evidence of computer control through desktop accessibility APIs. - ADK-Rust documentation describes a broader modular agent framework covering agents, tools, sessions, workflows, browser automation, guardrails, observability, and feature-gated services. The page reviewed documented version 2.2.0. It can inform orchestration choices, but does not establish a universal operating-system accessibility backend.
Versions, feature flags, and API support can change. Check the current crate documentation and implementation for the specific backend capabilities you need before depending on them.
Test adapters and the loop independently
Separate tests for normalization, policy, adapter behavior, and end-to-end state verification. A deterministic fake adapter is useful for testing the core without sending real input; platform integration tests should run only in controlled environments with explicit targets and a reliable stop path.
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- Contract tests: Check that every observation has a unique identity, declares its geometry and source, and retains required native properties. Ensure action decoding rejects unknown kinds and malformed parameters.
- Staleness tests: Change or remove a target after capture and confirm that an action based on the old observation is rejected rather than redirected.
- Policy tests: Verify allowed, confirmation-required, and blocked cases, including that a blocked action never reaches the adapter.
- Failure tests: Simulate timeouts, unavailable APIs, permission denial, unsupported actions, and ambiguous outcomes. Confirm that errors remain visible to the planner and user.
- Verification tests: Simulate an action that is dispatched but does not change the UI. The loop should take a new observation and re-plan or stop, not report success.
- Containment tests: Confirm that the configured sandbox, target restrictions, user interruption, and execution limits actually constrain adapter behavior.
Track outcomes such as rejected stale actions, policy blocks, adapter failures, and verified goal completion for your own supported tasks. Do not infer general accuracy or latency from a small application-specific test set. The sources cited here do not establish independent comparative performance figures for computer-control systems.
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