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Autonomous AI System Agent for Windows

HeimerClean is a bounded, local system agent for Windows. It is not designed around the traditional "scan, show a long list, and ask the user to decide" pattern. Its job is narrower and more operational: observe technical conditions, decide whether one of a small number of registered maintenance actions is useful, execute only when safety rules permit it, and measure what changed.

What autonomous means here

Autonomy means that low-risk, registered actions can be selected without requiring the user to interpret every process or temporary file. It does not mean unrestricted control of Windows, general-purpose command generation, or permission to inspect personal content.

A simple example

Imagine a user opening a game while memory pressure is rising and temporary files have accumulated. A conventional cleaner may start a scan at the wrong time. HeimerClean first recognizes the interactive workload. The safety layer can remove cleanup actions from the available set and leave only a low-impact quality-of-service adjustment or no action. When the device returns to a suitable state, the next cycle can reconsider maintenance.

For an office user, the same flow may reach a different conclusion. If no game or render session is active and memory or temporary-data pressure crosses a safe threshold, the agent can select a registered maintenance action, run it locally, and record CPU, memory, and temporary-data measurements before and after the operation.

The decision chain

Windows technical signals
        ↓
Local context vector
        ↓
Deterministic action mask
        ↓
Rule / Random Forest / constrained LinUCB selection
        ↓
Local registered action or no action
        ↓
Immediate and delayed outcome records
        ↓
Evidence, model training, and controlled rollout

1. Local technical context

The current context encoder converts runtime measurements into an 11-value vector. It contains a bias value plus normalized CPU load, RAM load, temporary-data volume, an energy-to-CPU-capacity ratio, GPU load, interactive-load score, game-session state, render-session state, NVIDIA GPU presence, and a RAM/temporary-data interaction value.

This is system-state context, not user-content context. Documents, e-mails, images, messages, and browser-page contents are not inputs to the decision model. See Local Context and Privacy.

2. Deterministic safety boundary

Before a model can choose anything, an action mask calculates which registered actions are allowed. The current registry contains:

Action Purpose Relative risk
none Wait and leave the system unchanged Minimal
qos_only Apply a bounded CPU-priority stabilization strategy Low
temp_clear Run registered temporary-data maintenance Low
ram_clear Run the registered memory-pressure routine Medium

Unknown actions are rejected. none is always available. Heavy foreground, game, render, or GPU activity can remove cleanup actions before any learning policy sees them. A blocked action cannot be re-enabled by the model. This is the central rule behind Safe Action Selection.

3. Hybrid selection

HeimerClean currently combines three decision mechanisms:

  1. Deterministic rules handle already-understood conditions and provide the safe baseline.
  2. A 50-tree Random Forest classifier distinguishes between memory and temporary-data interventions when verified training data is sufficient and the deterministic path delegates the decision.
  3. A constrained LinUCB contextual-bandit policy scores the actions that survived the safety mask. The score combines expected reward, uncertainty, and an action-risk penalty. Low-support cleanup recommendations can fall back to none or qos_only when their scores are close.

Random Forest is used for classification; LinUCB is used for adaptive choice among a small action set. Neither model can create a new command or bypass the registered action boundary.

4. Local execution

The final action is executed by the Windows background-service layer. Temporary-data maintenance, memory-pressure relief, and quality-of-service stabilization use separate registered routines. Windows itself schedules threads using process priority classes and thread priority levels; HeimerClean's quality-of-service path operates within that Windows scheduling model rather than replacing the operating-system scheduler.

5. Outcome recording

The agent records a local before/after snapshot and computes a bounded result from changes in CPU, RAM, and temporary-data volume. A second measurement can be scheduled after a delay so the short resource cost of running an optimization is not confused with a lasting improvement. The complete lifecycle is described in Outcome Verification and Policy Lifecycle.

Operating modes and maturity

Capability Current implementation state
Local technical context collection Operational
Deterministic rules and action mask Operational
Registered local action execution Operational
Immediate before/after records Operational
Delayed verification record Implemented; validation coverage is expanding
Random Forest classification Implemented with minimum-data and class-diversity gates
LinUCB recommendation Implemented
Shadow and guarded-live control paths Implemented
Proven superiority over the deterministic baseline Not claimed; controlled evidence is still required

The distinction is important. The architecture is implemented, but the existence of a learning path is not proof that it performs better. HeimerClean therefore separates implemented capability, controlled rollout, and validated superiority.

How the design is evaluated

Internal records show what the agent observed, which policy source proposed the action, which actions were allowed, what finally ran, and the immediate or delayed technical outcome. Independent Windows validation can use Microsoft Windows Performance Recorder and Windows Performance Analyzer, which are designed to capture and analyse operating-system and application performance traces.

The product claim is therefore not "AI always makes the computer faster." The testable claim is: within a registered and safety-filtered action space, the agent can choose, execute, and measure a maintenance response without requiring the user to diagnose the machine manually.

References