A Native AI Closed-Loop Execution Method and System Based on OpenClaw

Abstract

This invention belongs to the field of artificial intelligence communication technology and discloses an AI native closed-loop execution method and system based on OpenClaw. This method achieves multi-level, fine-grained autonomous decomposition of complex instructions by constructing a natural language instruction autonomous decomposition rule system; it designs a native triggering mechanism for execution layer sandbox practice and native error correction rules for execution anomalies to ensure the safety and fault tolerance of practical execution within the sandbox; and it develops a native linkage mechanism between result feedback and memory modules to achieve real-time memory updates and iterative optimization driven by execution results. This invention breaks through the barrier that traditional conversational AI can only generate information, realizing a native leap from "information generation" to "task execution" in AI. It can complete the autonomous closed-loop execution of complex system-level practical tasks without human intervention, ensuring the autonomy, continuity, and scalability of task execution. It is applicable to various AI practical application scenarios such as office automation, industrial intelligent operation and maintenance, digital employees, and cross-platform task scheduling.

Description

Technical Field

[0001] This invention belongs to the field of information technology, specifically relating to an AI native closed-loop execution method and system based on OpenClaw. Background Technology

[0002] OpenClaw, as an AI framework with native closed-loop execution capabilities, provides fundamental support for AI to achieve autonomous execution through its core logic of "instruction reception - intelligent decomposition - practical execution - result feedback." However, traditional execution methods based on OpenClaw have not yet broken through the barrier of conversational AI "only talking and not doing," exposing many specific and non-macro-level technical problems in complex task execution scenarios, and failing to achieve native closed-loop execution without human intervention. Specific problems are as follows: Problem 1: Natural language instruction decomposition relies on manual intervention, resulting in imprecise decomposition granularity and dependency resolution. Traditional methods require manual pre-setting of decomposition rules for complex natural language instructions (such as "deploy a Python environment on a Windows server, install the Django framework, start a web service, and verify port availability"), lacking autonomous decomposition capabilities. Furthermore, the decomposition granularity cannot adapt to task complexity; overly fine decomposition of simple tasks leads to low efficiency, while overly coarse decomposition of complex tasks results in execution failure. Additionally, the methods fail to effectively resolve dependencies between atomic tasks (such as "the framework can only be installed after the environment is deployed"), which can easily lead to problems such as disordered task execution order and resource conflicts, requiring manual adjustment of the decomposition results.

[0003] Question 2: The execution layer lacks a native sandbox triggering mechanism, resulting in low execution security and a lack of autonomous error correction capabilities. Traditional execution layers directly execute the decomposed tasks within the host system without building an isolated sandbox environment, making them prone to host system damage due to execution errors. Furthermore, task execution triggering relies on manually configured timed / manual triggering rules, lacking a native triggering mechanism based on task dependencies and unable to automatically wait for prerequisites to be met. In addition, when anomalies occur during execution (such as failed interface calls or incorrect parameters), there is no autonomous error correction capability, requiring manual intervention for troubleshooting and correction, leading to task execution interruptions and poor continuity.

[0004] Question 3: There is no linkage mechanism between the execution result and the memory module, making it impossible to achieve autonomous iterative optimization. The execution results of traditional methods are only used to generate feedback reports and are not linked with the memory module. Successful execution experience, failure exceptions, and error correction strategies cannot be stored in the memory module. The memory module only statically stores preset rules and lacks real-time updates and iterative optimization logic. This means that the same errors may still occur when similar tasks are executed repeatedly, and the autonomous iteration of "execution-learning-optimization" cannot be achieved. The accuracy and efficiency of task execution cannot be continuously improved.

[0005] Supplementary Explanation In addition to the three core issues mentioned above, the closed-loop execution logic of traditional methods is incomplete, only covering the basic process of "instruction reception-disassembly-execution-feedback" and not including the key links of "error correction-memory update-iterative execution". As a result, AI can only complete simple, error-free task execution. When faced with complex system-level practical tasks, a lot of manual intervention is required, and it cannot achieve truly autonomous closed-loop execution. Summary of the Invention

[0006] The purpose of this invention is to overcome the technical shortcomings of traditional OpenClaw-based AI execution methods, which are characterized by "talking without doing," reliance on manual intervention, and lack of autonomous iteration. It provides a native closed-loop execution method and system for AI based on OpenClaw. Based on OpenClaw's native "instruction reception - intelligent decomposition - practical execution - result feedback" closed-loop execution logic, and addressing the core problems raised in the background technology—namely, "instruction decomposition relying on manual intervention, execution lacking safety triggering and autonomous error correction, and lack of linkage between results and memory"—it designs four core technologies to construct a native autonomous iterative closed loop of "instruction reception - intelligent decomposition - sandbox execution - anomaly correction - result feedback - memory update - iterative execution." The specific invention content is as follows: S1: A natural language instruction autonomous decomposition rule system that enables precise decomposition without human intervention. 1. Construct a multi-level, adaptive natural language instruction autonomous decomposition rule system. Breaking through the limitations of traditional manual decomposition, and leveraging OpenClaw's decision-making capabilities, a natural language instruction autonomous decomposition rule system is constructed: Task complexity adaptive classification: Automatically identifies the complexity level of instructions (simple / medium / complex / system level) and matches the corresponding decomposition granularity to avoid overly coarse or overly fine decomposition; Multi-level dependency resolution and decomposition: According to the hierarchical decomposition rule of "general task → sub-task → atomic task", combined with the task dependency resolution rule (sequence / condition / resource dependency), a sequence of atomic tasks with dependencies is generated; Intelligent verification of disassembly results: By verifying the disassembly results, the integrity, executability, and conflict-free nature of atomic tasks are ensured, and automatic re-disassembly is performed when the conditions are not met. Core decomposition formula modeling: It integrates three dimensions: granularity adaptation, task dependency weighting, and decomposition verification. The core formula quantifies the decomposition process to ensure the accuracy of the decomposition results.

[0007] The core innovation lies in the integrated autonomous decomposition logic of adaptive granularity, dependency resolution, and intelligent verification, which can complete the accurate decomposition of complex instructions without human intervention, solving the problems of traditional decomposition relying on manual labor and inaccurate granularity.

[0008] S2: Sandbox practice with native triggers and native error correction rules to achieve safe and fault-tolerant practical execution. 2: Design a native triggering mechanism and a native error correction rule library for sandbox practice. To address the issues of low security and lack of autonomous error correction at the execution layer, an innovative sandbox-based native triggering mechanism and a native error correction rule base for execution exceptions are designed: Sandbox Practical Native Triggering: Construct an isolated sandbox environment, based on atomic task transformation, execution triggering conditions, and sandbox environment adaptation rules, to achieve automatic and secure triggering of task execution, granting only the minimum necessary permissions to ensure the security of the host system; Native error correction for execution exceptions: It has preset exception classification, error correction strategy matching, and error correction termination rules. It can identify the type of execution exception in real time and automatically match the error correction strategy (retry, parameter correction, path switching). When the same task fails to correct consecutive errors, the termination rule is triggered, which solves the problem of manual intervention required for traditional execution exceptions.

[0009] The core innovation lies in the execution layer design of native triggering + autonomous error correction, which realizes the safety and fault tolerance of practical execution and ensures the continuity of task execution.

[0010] S3: A native linkage mechanism between result feedback and memory update enables autonomous iterative optimization. 3: Native linkage mechanism between R&D result feedback and memory module Breaking through the limitations of traditional methods that disconnect results from memory, we developed a native linkage mechanism between result feedback and memory updating: Hierarchical memory storage: Execution data is stored in layers according to the dimensions of "short-term / long-term / scenario" to adapt to different use cases and timeliness requirements; Execution-driven memory update: Memory updates are automatically triggered when a task is completed or terminated. Successful experiences are weighted, and reasons and strategies are recorded for failed cases. Iterative optimization of memory: Regularly clean, merge, and upgrade memory data, and precipitate execution experience into execution rules to improve the ability to process subsequent tasks.

[0011] The core innovation lies in the real-time updating and iterative optimization of memory driven by execution results, which solves the problem of traditional memory being static and unable to iterate, and enables AI to achieve autonomous growth through "execution-learning-optimization".

[0012] Closed-loop logic integration: native autonomous iterative closed-loop, achieving autonomous execution throughout the entire process. S4: Construct a "7-step" native autonomous iterative closed loop to achieve closed-loop execution without human intervention. Beneficial effects: Integrating the above, and based on OpenClaw's native closed-loop logic, it is expanded into a 7-step native autonomous iterative closed loop: **"Instruction Reception - Intelligent Decomposition - Sandbox Execution - Anomaly Correction - Result Feedback - Memory Update - Iterative Execution"**. The decision-making layer, based on the updated memory module, automatically triggers secondary decomposition and execution for incomplete or optimizable tasks until the task is completed or a termination rule is triggered. The core innovation lies in the complete autonomous iterative closed-loop design, covering the entire process from instruction reception to iterative optimization, achieving native closed-loop execution without human intervention. Attached image description: Figure 1 Flowchart illustrating the working principle of this invention. Detailed Implementation

[0013] The method of the present invention will be described in detail below with reference to four specific embodiments.

[0014] Example 1: Autonomous Decomposition and Implementation of Natural Language Instructions (including core formulas) This embodiment corresponds to 1, solving the problems of traditional instruction decomposition relying on manual labor and lacking granularity. It includes a core decomposition formula, and the specific steps are as follows: Multimodal command reception and standardization The instruction receiving module receives natural language instructions input by the user, such as: "Deploy a MySQL database in the sandbox environment of the Linux server, configure remote access permissions, test connection availability, and restart the server and retry if the test fails." This standardizes the instruction format and generates a unified instruction object to be executed. .

[0015] Task complexity level identification The intelligent task breakdown module, based on task complexity grading rules, identifies that the instruction contains multiple dependent tasks such as "deploying the database, configuring permissions, testing connections, and conditional retries," classifying it as a complex task and matching it accordingly. (Simple level = 1, Medium level = 2, Complex level = 3, System level = 4).

[0016] Task dependency resolution and preliminary decomposition According to the multi-level disassembly rules, it is disassembled as follows: Overall task → Subtask 1 (Deploy MySQL), Subtask 2 (Configure remote permissions), Subtask 3 (Test connection), Subtask 4 (Retry after failure); Subtask 1 → Atomic Tasks 1.1 (Download MySQL installation package), 1.2 (Install MySQL), 1.3 (Initialize configuration); Subtask 2 → Atomic Tasks 2.1 (modify the my.cnf configuration file), 2.2 (restart the MySQL service); Subtask 3 → Atomic Task 3.1 (Execute the connection test script); Subtask 4 → Atomic Tasks 4.1 (Judgment of test results), 4.2 (Restarting the server), 4.3 (Re-execution of Subtask 3); Identify task dependencies: Atomic tasks 1.1→1.2→1.3→2.1→2.2→3.1→4.1; if task 4.1 fails, tasks 4.2→4.3 are triggered, generating a task dependency weight matrix. (Sequential dependency weight = 0.8, conditional dependency weight = 0.2).

[0017] Disassembly result verification implement Disassembly result verification: ① Completeness: The verification found that it covered all requirements of the overall task and met the requirements; ② Executability: Verification shows that all atomic tasks can be executed in a Linux sandbox environment, meeting the requirements; ③ No conflict: The verification task dependencies have no circular conflicts, which meets the requirements.

[0018] The core formula is used to calculate and generate the final atomic task sequence. Set disassembly verification coefficient Through the core decomposition formula Calculate and generate the final atomic task sequence with dependencies: [1.1→1.2→1.3→2.1→2.2→3.1→4.1 (failure→4.2→4.3)].

[0019] Disassembly results output The final atomic task sequence is output to the sandbox execution module, ready to trigger the actual execution.

[0020] Algorithm efficiency enhancement principle: Modeling innovation: The core decomposition formula quantifies qualitative decomposition rules into a mathematical model, integrating three core dimensions: granularity adaptation, task dependency, and decomposition verification, to ensure the accuracy of the decomposition results; the multi-level dependency resolution rule solves the problem of traditional decomposition ignoring task dependencies.

[0021] Efficiency improvement: Compared with traditional manual disassembly, the disassembly time of complex instructions is reduced from several minutes to several seconds, the executability of the disassembly results is improved, and the execution failure rate caused by dependency conflicts is significantly reduced; disassembly can be completed without human intervention, greatly reducing labor costs.

[0022] Example 2: Sandbox Practice: Implementation of Native Triggering and Execution Exception Correction This embodiment corresponds to step 2, addressing the issues of low security and lack of autonomous error correction in traditional execution. The specific steps are as follows: Sandbox environment initialization and atomic task conversion The sandbox execution module builds an independent, isolated sandbox environment for Linux servers, configuring only the minimum necessary permissions including "file operations, software installation, and service start / stop". Following the atomic task conversion rules, atomic task 1.1 (downloading the MySQL installation package) is converted into an executable Shell command within the sandbox: "wget ​​https: / / cdn.mysql.com / Downloads / MySQL-8.0 / mysql-8.0.36-linux-glibc2.28-x86_64.tar.xz".

[0023] Sandbox practice with native trigger execution Based on the execution triggering condition rules, the atomic task 1.1 is automatically triggered if it has no prerequisites and sufficient sandbox resources. The native sandbox environment adaptation rules temporarily open the sandbox's network access permissions according to the task type (network download), and automatically close them after execution.

[0024] Execution of exception identification and error correction triggering During the execution of atomic task 1.1, a "download timeout" exception occurred. The native error correction module, based on exception classification rules, identified it as an environment exception; matching the error correction strategy matching rules, it selected the "retry + switch download source" strategy: ① Initial error correction: Re-execution of the original download command still timed out; ② Secondary error correction: Switch the download source to a domestic mirror and execute the command: "wget ​​https: / / mirrors.tuna.tsinghua.edu.cn / mysql / downloads / MySQL-8.0 / mysql-8.0.36-linux-glibc2.28-x86_64.tar.xz"; ③ The second error correction was successful, atomic task 1.1 was completed, and the next atomic task 1.2 was automatically triggered.

[0025] Error correction termination rule verification Assuming that atomic task 1.1 fails to correct errors three times consecutively (retry at the original address, domestic mirror, and backup mirror), the native error correction module triggers the error correction termination rule, terminates the execution of the task, and sends an exception message "MySQL installation package download failed" to the decision-making level. No forced manual intervention is triggered; only a prompt message is generated.

[0026] Subsequent tasks executed normally. After atomic task 1.1 is executed successfully, subsequent atomic tasks 1.2-3.1 will be triggered and executed in sequence. The sandbox execution module collects the execution status (installation progress, configuration results, test results) in real time to ensure the continuity of task execution.

[0027] Algorithm efficiency enhancement principle: Architectural innovation: The isolated sandbox environment ensures the security of the host system at the physical level, and the minimum necessary permission configuration avoids the risk of unauthorized operation; the native triggering mechanism realizes the automatic execution of tasks without the need for manual configuration of triggering rules.

[0028] Algorithm efficiency: The native error correction rule library for execution exceptions enables autonomous error correction without human intervention, improving the efficiency of resolving the same exception and significantly reducing the task termination rate caused by exceptions; the error correction termination rule avoids ineffective infinite error correction and ensures the rational use of system resources.

[0029] Example 3: Implementation of Native Linkage Update Between Result Feedback and Memory Module This embodiment corresponds to step 3, addressing the problem of traditional results being disconnected from memory and unable to be iteratively optimized. The specific steps are as follows: Execution result collection and standardized feedback The results feedback module collects the execution results of this task: ① Successful items: Atomic tasks 1.1 (successfully executed after switching download sources), 1.2, 1.3, 2.1, and 2.2 were executed successfully; ② Anomaly: The first test connection of Atomic Task 3.1 failed (incorrect permission configuration), but was successful after correction; ③ Final result: The overall task was successfully executed, the MySQL database was deployed and can be accessed remotely; Generate a standardized feedback report and send it to the memory management module.

[0030] Memory module hierarchical update The memory management module uses a native linkage mechanism to complete layered updates: ① Short-term memory: Stores temporary data for this execution (download source address, installation path, configuration parameters), and automatically clears it 48 hours after the task is completed; ② Long-term memory: Store successful execution rules ("MySQL downloads in the Linux sandbox environment prioritize using the Tsinghua mirror source" and "Remote access permission configuration requires modifying bind-address to [0.0.0.0](0.0.0.0)"), and strengthen the weight of the corresponding rules; ③ Contextual memory: Store the exception cases of this execution ("The test connection failed because of incorrect permission configuration. The error correction strategy is to modify my.cnf and restart the service") and add them to the exception case library.

[0031] Iterative optimization of memory execution The memory management module periodically (every day at midnight) iterates and optimizes the memory data: ① Cleaning: Delete expired temporary data from short-term memory; ② Integration: Merge similar cases of "MySQL connection failure" from the contextual memory and summarize general error correction strategies; ③ Upgrade: Upgrade the "Tsinghua mirror source download" rule in long-term memory to the default execution rule, and apply it to subsequent similar tasks.

[0032] Iterative optimization effect verification When the user sends the "Deploy MySQL Database" command again, the intelligent disassembly module, based on the updated long-term memory, directly sets the download source of Atomic Task 1.1 to the Tsinghua mirror source, avoiding download timeout exceptions and improving task execution efficiency.

[0033] Algorithm efficiency enhancement principle: Mechanism Innovation: The native linkage mechanism between result feedback and memory update realizes a closed loop of "execution result → memory accumulation → rule optimization", enabling AI to have "learning" ability; hierarchical memory storage adapts to the usage characteristics of different data and improves the utilization efficiency of memory data.

[0034] Efficiency enhancement: Compared with traditional static memory, the execution error rate of similar tasks is significantly reduced, and the execution efficiency is improved; the accumulation and integration of abnormal cases continuously enhance the error correction capability of AI, and realize autonomous iterative optimization.

[0035] Example 4: Full-process native autonomous iterative closed-loop implementation This embodiment corresponds to step 4, verifying the execution effect of the "7-step" native autonomous iterative closed loop. The specific steps are as follows: Command reception and intelligent disassembly (Step 1-2) Upon receiving the user instruction "Deploy MySQL database and test connection", the system autonomously decomposes the task according to the process in Example 1, generates an atomic task sequence, and outputs it to the sandbox execution module.

[0036] Sandbox execution and exception handling (Steps 3-4) Following the process in Example 2, the sandbox practice was executed using native triggering. Atomic task 1.1 encountered a download timeout exception, and the native error correction module completed autonomous error correction to ensure task execution.

[0037] Results feedback and memory update (stages 5-6) The execution result feedback and memory module hierarchical update were completed according to the process in Example 3. The long-term memory added the rule of "prioritizing the use of Tsinghua mirror source", and the context memory recorded error correction cases of download timeout.

[0038] Autonomous Iterative Closed-Loop Triggering (Step 7) The decision-making level verifies the overall task execution result: Although the execution was ultimately successful, atomic task 1.1 encountered an anomaly, indicating room for optimization and triggering a self-iterative closed loop. ① Based on the updated memory module, the instructions were re-disassembled, and the download source for Atomic Task 1.1 was directly set to the Tsinghua mirror source; ② Simulate the execution of a new atomic task sequence to verify that there are no download timeout exceptions and that execution efficiency is improved; ③ The decision-making level records the results of the iterative optimization, updates them to long-term memory, and completes this iteration.

[0039] Iteration effect verification When the user sends the same command again, the intelligent disassembly module directly uses the optimized atomic task sequence, the sandbox execution module executes without any abnormalities, and the task execution time is shortened compared to the first time, realizing the autonomous iteration of "execution-optimization-re-execution".

[0040] Principles of architecture and algorithm efficiency enhancement: Closed-loop innovation: The 7-step native autonomous iterative closed loop covers the entire process from instruction reception to iterative optimization, enabling the execution and optimization of complex tasks without human intervention, breaking through the barrier of traditional conversational AI that "only talks and does nothing". System efficiency improvement: Compared with the traditional OpenClaw basic closed-loop logic, the extended closed loop of this invention realizes the autonomy, coherence and iteration of task execution, improves the completion rate of system-level practical tasks, reduces the execution error rate, and can complete task extension without additional reliance on external systems.

[0041] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. An AI-native closed-loop execution method based on OpenClaw, characterized in that, Based on OpenClaw's native "instruction reception - intelligent decomposition - practical execution - result feedback" closed-loop execution logic, autonomous iterative execution without human intervention is achieved, including the following steps: a. Native command reception: Construct a multimodal command reception interface to receive input commands of various types, such as natural language, structured, and visual, complete command format standardization and validity verification, and generate a unified command object to be executed; b. Autonomous Decomposition of Natural Language Instructions: Based on a pre-defined autonomous decomposition rule system, natural language instructions are decomposed into multi-level, fine-grained components. The core decomposition formula is: Tsplit=RgranIstdLtask⊙Wdep+γ CcheckTtemp, where Tsplit is the decomposed atomic task sequence, and Rgran To adapt the decomposition operation to granularity, Istd is the standardized instruction object, Ltask is the task complexity level, ⊙ is the task dependency weighted operation, Wdep is the task dependency weight matrix, γ is the decomposition verification coefficient, and Ccheck is used. For the disassembly result verification operation, Ttemp is a temporary disassembly task sequence; c. Native triggering and execution of sandbox practice: Based on the native triggering mechanism of sandbox practice, the decomposed atomic task sequence is converted into executable operation instructions within the sandbox, triggering the practice execution in the isolated sandbox environment, and collecting execution status and abnormal information in real time; d. Native error correction for execution exceptions: A pre-defined native error correction rule library for execution exceptions is used to match the type of execution exception in real time and automatically select an error correction strategy (retry, replace execution path, adjust parameters) to complete autonomous error correction without human intervention; e. Native feedback of execution results: Collect the final results of the practical execution within the sandbox, feed them back to the decision-making level in a standardized format, and generate a task execution result report; f. Real-time update of memory module: Based on the native linkage mechanism of result feedback and memory update, the execution result, exception information, error correction strategy and other data are written into the memory module to complete the hierarchical update and iterative optimization of short-term memory, long-term memory and contextual memory; g. Autonomous Iterative Closed Loop: Based on the updated memory module, the decision-making layer further decomposes and executes incomplete / optimized tasks, forming a native autonomous iterative closed loop of "instruction reception - intelligent decomposition - practical execution - exception correction - result feedback - memory update - iterative execution".

2. The method according to claim 1, characterized in that, The natural language instruction autonomous decomposition rule system includes: a. Task complexity classification rules: Tasks are divided into four complexity levels: simple (single atomic task), medium (multi-atomic task with no dependencies), complex (multi-atomic task with sequential / conditional dependencies), and system-level (cross-platform / cross-module multi-dependency task), and Ltask is automatically matched; b. Multi-level decomposition rule: Complex / system-level tasks are decomposed into directly executable atomic task granularity according to the hierarchical decomposition rule of "general task → sub-task → atomic task"; c. Task dependency resolution rules: Identify sequential dependencies (e.g., "execute B after A is completed"), conditional dependencies (e.g., "execute B if A succeeds, otherwise execute C"), and resource dependencies (e.g., "A can only be executed if device X is occupied") between atomic tasks, and generate a task dependency weight matrix Wdep; d. Decomposition result verification rules: Verify the completeness of atomic tasks (covering all requirements of the total task), executability (can be implemented within the sandbox), and conflict-free nature (task dependencies have no circular conflicts). If these conditions are not met, trigger re-decomposition.

3. The method according to claim 1, characterized in that, The native triggering mechanism for sandbox practice includes: a. Atomic task conversion rules: Convert the decomposed atomic tasks into operation instructions that can be recognized within the sandbox (such as API calls, script execution, and interface operations), and adapt them to the execution environment and permissions within the sandbox. b. Execution trigger condition rules: Execution is automatically triggered when the task dependency conditions (prerequisite tasks completed, resources available) are met; otherwise, it enters the waiting queue and the trigger conditions are monitored in real time. c. Native Sandbox Environment Adaptation Rules: Dynamically adjust sandbox permissions and resource configurations based on atomic task type (file operation, network request, device control), granting only the minimum necessary permissions to ensure execution security.

4. The method according to claim 1, characterized in that, The native error correction rule base for execution exceptions includes: a. Exception Classification Rules: Execution exceptions are classified into three main categories: environment exceptions (insufficient sandbox resources, unavailable interfaces), operation exceptions (incorrect command format, invalid parameters), and result exceptions (execution results do not meet expectations); b. Error correction strategy matching rules: Environment anomalies match the "resource expansion, interface retry, switch to backup resources" strategy; operation anomalies match the "instruction rewriting, parameter correction, format conversion" strategy; result anomalies match the "re-execute, adjust execution parameters, change execution path" strategy. c. Error correction termination rule: If the same atomic task fails to correct errors ≥ 3 times consecutively, the task execution will be terminated, the abnormal information will be reported to the decision-making level and a manual intervention prompt will be triggered (non-mandatory intervention).

5. The method according to claim 1, characterized in that, The native linkage mechanism between result feedback and memory update includes: a. Memory hierarchical storage rules: Temporary data during execution (such as execution progress and intermediate results) are stored in short-term memory, atomic task rules and parameter configurations that are successfully executed are stored in long-term memory, and execution processes and abnormal cases in specific scenarios are stored in contextual memory. b. Memory update triggering rules: Memory update is automatically triggered when execution is completed / terminated. If the execution is successful, the weight of the corresponding memory data is strengthened. If the execution fails, the reason for the exception and the error correction strategy are recorded. c. Memory Iteration Optimization Rules: Regularly clean (delete invalid data), merge (merge similar cases), and upgrade (optimize execution rules) long-term memory and episodic memory to improve the accuracy of subsequent task breakdown and execution.

6. The method according to claim 1, characterized in that, The triggering conditions for the native autonomous iterative closed loop include: a. The overall task is not completed (some atomic tasks failed to execute and cannot be corrected); b. The execution result meets expectations but there is room for optimization (e.g., low execution efficiency, high resource consumption); c. After the memory module is updated, the rules for disassembling / executing similar tasks are optimized; Once triggered, the decision-making layer re-executes steps 2-6 based on the updated memory data until the task is completed or the error correction termination rule is triggered.

7. An AI-native closed-loop execution system based on OpenClaw, characterized in that, To implement the method of claims 1-6, the method comprises: a. Command receiving module: Constructs a multimodal interface to complete the reception, standardization, and validity verification of multiple types of commands; b. Intelligent disassembly module: Equipped with a natural language command-based autonomous disassembly rule system, it executes core disassembly formulas to complete the autonomous disassembly of complex commands; c. Sandbox Execution Module: Equipped with a native triggering mechanism for sandbox practice, it constructs an isolated sandbox environment to complete the practical execution of atomic tasks; d. Native error correction module: Equipped with a native error correction rule library for execution exceptions, enabling autonomous identification and correction of execution exceptions; e. Results Feedback Module: Collects execution results and generates standardized feedback reports; f. Memory Management Module: Based on the native linkage mechanism, it completes the hierarchical storage, real-time updates and iterative optimization of the memory module; g. Closed-loop management module: Integrates the above modules to trigger a native autonomous iterative closed loop, ensuring the autonomy and continuity of task execution.

8. The system according to claim 7, characterized in that, The isolated sandbox environment of the sandbox execution module supports dynamic creation and destruction. Each atomic task can be assigned an independent sandbox environment to avoid mutual interference between tasks. The memory management module supports visual management of memory data and custom update rule configuration.

9. The system according to claim 7, characterized in that, The intelligent decomposition module's autonomous decomposition rule system supports dynamic expansion. When adding a new task type, only the corresponding complexity classification rules and decomposition rules need to be added, without modifying the module's core logic. The native error correction module's error correction rule library supports self-learning optimization based on historical abnormal cases.

10. An electronic device, characterized in that, It includes a processor and a memory, the memory storing processor-executable instructions, and the processor executing the instructions to implement the AI ​​native closed-loop execution method based on any one of claims 1 to 6.

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