An intelligent agent application deployment verification and rollback control method, system, electronic device and storage medium

Abstract

This invention relates to the field of artificial intelligence application deployment and software operation control technology, specifically to a method, system, electronic device, and storage medium for intelligent agent application deployment verification and rollback control. The method acquires the network, data source, permissions, model endpoints, and audit constraints of the deployment environment, and generates a deployment capability profile and deployment strategy package according to fixed field transformation relationships. It collects decision trajectories, tool calls, retrieval references, recovery records, and audit records. It performs standardized encoding and hash calculation on records for each partition, binding partition summaries with source evidence. It verifies and recalculates the deployment verification status through structure, summary, policy, permission context, replay conditions, and acceptance indicators. When rollback is required, it verifies the rollback pointer, state recovery pointer, and tool replay restrictions, generating a rollback plan or exercise report. Inconsistent summaries, missing permission contexts, or insecure tool replay conditions trigger rollback requirements, acceptance failures, or rollback blocking information, respectively.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence application deployment and software operation control technology, specifically to a method, system, electronic device, and storage medium for intelligent agent application deployment verification and rollback control. Background Technology

[0002] When intelligent agent applications are deployed in different field environments, network egress, data sources, permission models, model endpoints, tool protocols, and audit requirements often differ. Existing deployment processes mostly rely on manual configuration and individual test results to determine deployment usability, lacking verifiable data correlation between environmental constraints, operational policies, and acceptance records.

[0003] When intelligent agents use both search enhancements and external tools, search results may become invalid due to changes in permission context or document version, and tool calls may also alter the state of external systems. If acceptance records, deployment policy packages, and rollback pointers are not consistently bound, it is difficult to determine the configuration and state to be restored after a deployment failure, and it is also difficult to prevent tool calls with side effects from being executed repeatedly.

[0004] Therefore, a technical solution is needed that can transform on-site environmental constraints into executable deployment strategies, perform summary binding and rule recalculation on deployment acceptance records, and verify rollback preparation conditions and tool replay limitations in the event of anomalies. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method, system, electronic device, and storage medium for intelligent agent application deployment verification and rollback control, thereby resolving the technical problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention discloses a method for intelligent agent application deployment verification and rollback control, comprising the following steps:

[0008] Obtain environmental constraint data for the target deployment environment, wherein the environmental constraint data includes at least network access constraints, data source constraints, permission constraints, model endpoint constraints, and audit constraints;

[0009] According to the preset field mapping rules, the environmental constraint data is converted into a deployment capability profile, which is used to represent model access capability, retrieval enhancement capability, tool access capability, state recovery capability and audit capability;

[0010] Based on the fixed field transformation relationship, a deployment strategy package is generated according to the deployment capability profile. The deployment strategy package includes at least the operation strategy, retrieval enhancement strategy, tool invocation strategy, recovery strategy, audit strategy, and verification control strategy.

[0011] Execute the deployment acceptance task according to the deployment strategy package, and collect decision-making traces, tool call records, retrieval reference records, status recovery records, and audit records;

[0012] Each collected record is standardized and hashed to generate deployment evidence data. The deployment evidence data includes partition summaries corresponding to decision trajectories, tool call records, retrieval reference records, state recovery records, and audit records, and includes gate inputs and rollback pointers.

[0013] According to the preset gate rules, the deployment evidence data is subjected to data structure verification, partition summary recalculation, source summary binding verification, strategy consistency verification, tool replay condition verification, and acceptance index verification, and the deployment verification status is output.

[0014] When the partition summary recalculation results are inconsistent, the deployment verification status is determined to require rollback. When the deployment verification status requires rollback, a rollback requirement is output, and a rollback plan is generated based on the rollback pointer and the evidence package identifier of the deployment evidence data. The state recovery pointer and tool replay strategy in the rollback plan are verified. When the state recovery pointer lacks a state snapshot summary, or there is a tool call with external side effects, allowing replay but lacking an idempotent key, the rollback requirement status is maintained and rollback blocking information is output.

[0015] In some embodiments, the environmental constraint data is obtained from machine detection records, confirmed environmental constraint lists, and manual approval records; the machine detection records and confirmed environmental constraint lists are respectively normalized and hashed to generate machine detection summaries and environmental constraint list summaries, and the workpiece references and approver identifiers of the machine detection summaries, environmental constraint list summaries, and manual approval records are associated and stored with the deployment capability profile.

[0016] In some embodiments, the preset field mapping rules include: generating a remote tool switch based on the public network egress status; generating a set of allowed protocols based on private network requirements and the public network egress status; generating a retrieval referencing strategy based on data persistence constraints and permission models; generating a model access status based on model endpoint reachability and credential referencing status; and generating a recovery strategy based on credential rebinding requirements.

[0017] In some embodiments, generating the deployment policy package includes: converting the public network egress status to remote tool on / off; limiting the allowed protocol set to local standard input / output protocols when the private network requirement is true or the public network egress status is off; converting the original content preservation restriction to a retrieval policy that only preserves the reference identifier; converting the role-based permission model to a retrieval policy that requires permission context; and converting the credential rebinding requirement to a recovery policy field.

[0018] In some embodiments, the normalized encoding includes sorting field names, using compact delimiters, uniformly using UTF-8 character encoding, and rejecting non-finite values; each partition summary is calculated based on the evidence package identifier, running identifier, partition type, and corresponding partition content; the integrity of the file containing the deployed evidence data or the frozen archive is further verified by the file summary or archive summary, which is not used as the top-level field of the deployed evidence data.

[0019] In some embodiments, the retrieval enhancement strategy requires each retrieval reference record to include at least a data source identifier, a content fragment identifier, a document version, and a non-empty permission context; when a role-based permission model requires a permission context and any retrieval reference record lacks the permission context, the corresponding permission verification result is set to failure.

[0020] In some embodiments, the tool invocation record includes at least a tool identifier, a side effect flag, an idempotent key, and a replay allowed flag, and the tool invocation policy includes a flag that prohibits non-idempotent autoreplay and an idempotent key requirement;

[0021] For tool calls that have external side effects, allow replay, and lack idempotent keys, set the tool replay condition check to fail and output rollback blocking information.

[0022] In some embodiments, the preset gate rules include at least data structure verification, digest consistency verification, permission context existence verification, sensitive information verification, policy consistency verification, acceptance indicator verification, and rollback preparation condition verification; and output one of the following deployment verification states according to a fixed deterministic priority: acceptance failure, rollback required, external condition blocking, manual approval required, or available for private review.

[0023] In some embodiments, the rollback pointer consists of a rollback identifier and a target deployment version summary. A rollback plan is generated based on the rollback pointer and the evidence package identifier of the deployment evidence data. The rollback plan includes a target evidence package identifier, a state recovery pointer, a tool replay strategy, and minimum acceptance requirements. The state recovery pointer includes a state snapshot location and a state snapshot summary. The tool replay strategy includes a flag prohibiting non-idempotent autoreplay and an idempotent key requirement. The rollback exercise generates an exercise report by verifying the existence of the above fields, freezing archive integrity, and tool replay restrictions, without concluding that the target deployment version or state snapshot has been actually restored.

[0024] This invention provides a method for intelligent agent application deployment verification and rollback control, which has the following beneficial effects:

[0025] When the field constraint fields change, the fixed field transformation relationship generates corresponding policy field changes, making the mapping results between constraints and policies reviewable.

[0026] When partition content or summary binding is modified, normalized summary recalculation produces inconsistent results, causing the gate status to be downgraded to acceptance failure or requiring rollback.

[0027] Permission verification fails when a role-based retrieval reference lacks a permission context or document version, and the reference cannot support a pass status.

[0028] When the side effects tool call allows replay but lacks an idempotent key, the tool's replay condition check fails and outputs a rollback blocking message.

[0029] When the rollback pointer, state recovery pointer, or frozen archive integrity check fails, the system maintains the rollback state and outputs rollback blocking information to avoid mistaking the field existence drill for a real recovery.

[0030] Secondly, this invention provides an intelligent agent application deployment verification and rollback control system, comprising:

[0031] The environmental constraint acquisition module is used to acquire environmental constraint data of the target deployment environment;

[0032] The deployment capability profiling module is used to convert the environmental constraint data into a deployment capability profile according to preset field mapping rules.

[0033] The strategy generation module is used to generate a deployment strategy package based on the deployment capability profile.

[0034] The acceptance execution module is used to execute deployment acceptance tasks according to the deployment strategy package and collect acceptance records.

[0035] The evidence data generation module is used to perform standardized encoding and hash calculation on each record partition of the acceptance record to generate a partition summary, and to assemble the gate input and rollback pointer to generate deployment evidence data;

[0036] The gate recalculation module is used to recalculate the deployment evidence data according to preset gate rules and output the deployment verification status;

[0037] The rollback preparation control module is used to generate a rollback plan based on the rollback pointer and the evidence package identifier of the deployment evidence data when the deployment verification state indicates that a rollback is required, verify the state recovery pointer and tool replay strategy in the rollback plan, generate a drill report, and maintain the rollback required state and output rollback blocking information when the rollback preparation conditions are not met.

[0038] Thirdly, the present invention provides an electronic device, the electronic device including a memory and a processor, the memory storing at least one computer-executable instruction, the processor being configured to run the computer-executable instruction, the computer-executable instruction being run by the processor to implement the intelligent agent application deployment verification and rollback control method described in the first aspect.

[0039] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the intelligent agent application deployment verification and rollback control method described in the first aspect.

[0040] Compared with the prior art, the beneficial effects of the intelligent agent application deployment verification and rollback control system, electronic device and storage medium of the present invention are the same as the beneficial effects of the intelligent agent application deployment verification and rollback control method described above, so they will not be repeated here. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the deployment verification and rollback preparation control method of the present invention;

[0042] Figure 2 This is a schematic diagram of the overall structure of the deployment verification and rollback control system of this invention;

[0043] Figure 3 A diagram illustrating the conversion relationship between environmental constraints, deployment capability profiles, and deployment strategy packages;

[0044] Figure 4 A diagram illustrating the deployment of evidence data structure and its summary binding relationships;

[0045] Figure 5 A schematic diagram of the rollback preparation and tool replay condition verification process. Detailed Implementation

[0046] The following embodiments are used to illustrate the technical solutions of the present invention and do not limit the scope of protection of the present invention. Each embodiment is a synthesized, anonymized, or hypothetical example and does not represent actual customer deployment, production environment recovery, or external verification. Unless otherwise specified, the hash algorithm may be SHA-256 or a hash algorithm with equivalent uses; policy packages and acceptance records may be stored in a structured data format.

[0047] Example 1: From Environmental Constraints to Deployment Verification Status

[0048] In an enterprise intranet deployment environment, the environment constraint acquisition module reads machine probing records and a confirmed list of environment constraints. Machine probing records include public network egress observations, allowed domain name observations, model endpoint reachability, and credential reference existence; the environment constraint list includes data source identifiers, permission models, original content retention restrictions, audit retention periods, and credential usage methods. The system calculates source summaries for both types of inputs.

[0049] The deployment capability profiling module processes inputs according to a field mapping table. For example, when the public network exit is unavailable, the remote network tool capability is set to disabled; when the data source requires role-based control and does not allow saving the original content, the retrieval enhancement capability is set to "require permission context, save only reference identifier"; when the model endpoint is reachable but the credential is only allowed to be injected at runtime, the model access capability is set to "reachable, verify credential reference before execution"; when the credential rebinding requirement is true, the recovery capability is set to "recoverable, rebind credential after recovery".

[0050] The strategy generation module reads the deployment capability profile and performs fixed field transformations. For example, when the public network exit is closed, the remote network tool switch is set to off; when the private network is required or the public network exit is closed, only local standard input / output protocols are allowed; when any data source prohibits saving original content, only saving reference identifiers is enabled; when role-based data sources exist, the retrieved reference records are required to carry permission context. The generated deployment strategy package includes model running strategy, retrieval enhancement strategy, tool invocation strategy, recovery strategy, auditing strategy, and verification control strategy. This implementation does not include strategy rule set selection, rule conflict resolution, rule version, or deployment capability profile summary fields.

[0051] The acceptance execution module executes or reads acceptance records such as model endpoint connectivity, retrieval references, permission negative examples, tool calls, state compatibility, and audit anonymization according to the deployment strategy package. For retrieval records, it saves the data source identifier, content fragment identifier, document version, and permission context; for tool records, it saves the tool identifier, side effect flag, idempotent key, and replay allowed flag. Example data is synthetic or anonymized data and does not contain original customer content or genuine credentials.

[0052] The evidence data generation module performs normalized encoding on each record. Normalized encoding includes sorting by field name, using a compact delimiter, uniformly using UTF-8 character encoding, and rejecting non-finite numerical values. The module calculates a partition summary by combining the evidence package identifier, run identifier, partition type, and partition content (excluding the summary field), and requires source evidence items to be bound to the corresponding partition summary. The integrity of frozen files or archives is separately verified by the file summary or archive summary.

[0053] The gate recalculation module recalculates and compares the summaries of each partition, and then performs structure verification, summary consistency verification, permission context existence verification, sensitive information verification, policy consistency verification, acceptance metric verification, and rollback preparation condition verification. The status priorities are fixed as: acceptance failed, rollback required, external condition blocked, manual approval required, and available for private review; the same input produces a definite output, and these priorities are fixed and not configured at runtime.

[0054] Example 2: Retrieval References and Permission Verification

[0055] In a role-based internal document retrieval scenario, user requests carry a permission context formed by the current user role and data access scope. The acceptance execution module receives the retrieval reference record and checks whether the retrieval reference record carries a non-empty permission context; this implementation does not re-execute the data source access permission determination; the retrieval reference record saves the data source identifier, content fragment identifier, document version, permission context, and relevance value, and does not write the restricted original text into the deployment evidence data.

[0056] During gate recalculation, the system checks whether a role-based data source exists at the deployment site and checks each retrieved reference record to ensure that the permission context is not empty. Schema verification also checks the required fields of the retrieved reference records. If the policy requires a permission context and any reference record lacks this field or is empty, the permission verification fails. This implementation does not reread the real-time permission configuration of the data source, nor does it compare the recalculated current permission context summary.

[0057] Example 3: Tool Recall Replay Control

[0058] The tool call log includes the tool identifier, side effect flag, idempotent key, and replay allowed flag. The policy by default disables non-idempotent automatic replay and requires idempotent keys; this embodiment only verifies whether the log and policy meet the above restrictions, does not automatically infer the actual side effects of external tools, and does not perform compensation operations.

[0059] The tool ledger stores the tool identifier, summary, side effect flag, idempotent key, and replay allowed flag. Rollback preparation checks read the tool ledger; if a record simultaneously indicates external side effects, replay allowed, and lacks an idempotent key, the tool replay condition check is set to fail and a blocking message is output. This implementation does not invoke the target tool, perform tool replay, or execute preset compensation operations.

[0060] Example 4: Rollback Preparation and Drill Condition Verification

[0061] The system reads the target deployment version summary of the rollback pointer in the deployment evidence data and reads the evidence package identifier from the deployment evidence data to construct a rollback plan; the evidence package identifier is written into the target evidence package identifier field of the rollback plan. The state snapshot location and state snapshot summary in the synthetic example are deterministically generated by the runtime identifier and are only used to verify field binding and validation processes; they cannot prove the existence or recovery of the corresponding real state snapshot.

[0062] The automated rollback drill reads the frozen archive list and recalculates the archive file digests, checking the rollback target digest, state snapshot digest, checkpoint digests in the state recovery record, the policy of prohibiting non-idempotent automatic replay, and minimum acceptance requirements. The "Policy Recovery" and "Snapshot Recovery" steps in the drill represent verification of the existence of security policy boolean values ​​and digests; they do not perform actual replacement of policies, configurations, or state snapshots. If the state snapshot digest, checkpoint digest, or insecure tool record is missing, the drill result is a failure or block; passing all conditions only indicates that the rollback preparation verification has passed.

[0063] This invention also provides an electronic device, which includes a memory and a processor. The memory stores at least one computer-executable instruction, and the processor is configured to run the computer-executable instruction. The computer-executable instruction is run by the processor to implement the above-described intelligent agent application deployment verification and rollback control method.

[0064] Figure Labels

[0065] 101: Environmental Constraint Acquisition Module; 102: Deployment Capability Profiling Module; 103: Strategy Generation Module; 104: Acceptance Execution Module; 105: Evidence Data Generation Module; 106: Gate Recalculation Module; 107: Rollback Preparation Control Module.

[0066] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product is stored in the storage medium and includes several instructions for causing a computer device to execute all or part of the steps of the methods of the various embodiments of the present invention. The storage medium includes media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0067] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for intelligent agent application deployment verification and rollback control, characterized in that, include: Obtain environmental constraint data for the target deployment environment, wherein the environmental constraint data includes at least network access constraints, data source constraints, permission constraints, model endpoint constraints, and audit constraints; According to the preset field mapping rules, the environmental constraint data is converted into a deployment capability profile, which is used to represent model access capability, retrieval enhancement capability, tool access capability, state recovery capability and audit capability; Based on the fixed field transformation relationship, a deployment strategy package is generated according to the deployment capability profile. The deployment strategy package includes at least the operation strategy, retrieval enhancement strategy, tool invocation strategy, recovery strategy, audit strategy, and verification control strategy. Execute the deployment acceptance task according to the deployment strategy package, and collect decision-making traces, tool call records, retrieval reference records, status recovery records, and audit records; Each collected record is standardized and hashed to generate deployment evidence data. The deployment evidence data includes partition summaries corresponding to decision trajectories, tool call records, retrieval reference records, state recovery records, and audit records, and includes gate inputs and rollback pointers. According to the preset gate rules, the deployment evidence data is subjected to data structure verification, partition summary recalculation, source summary binding verification, strategy consistency verification, tool replay condition verification, and acceptance index verification, and the deployment verification status is output. When the partition summary recalculation results are inconsistent, the deployment verification status will be determined to require rollback; When the deployment verification status is that rollback is required, a rollback requirement is output, a rollback plan is generated based on the rollback pointer and the evidence package identifier of the deployment evidence data, and the state recovery pointer and tool replay strategy in the rollback plan are verified. When the state recovery pointer lacks a state snapshot summary, or when there is a tool call with external side effects, allowing replay and lacking an idempotent key, maintain the state that needs to be rolled back and output rollback blocking information.

2. The method according to claim 1, characterized in that, The environmental constraint data is obtained from machine detection records, confirmed environmental constraint lists, and manual verification records. The machine detection records and confirmed environmental constraint lists are respectively normalized and hashed to generate machine detection summaries and environmental constraint list summaries. The workpiece references and signer identifiers of the machine detection summaries, environmental constraint list summaries, and manual verification records are associated and stored with the deployment capability profile.

3. The method according to claim 1, characterized in that, The preset field mapping rules include: generating remote tool switches based on public network egress status; generating a set of allowed protocols based on private network requirements and public network egress status; generating retrieval and referencing strategies based on data persistence constraints and permission models; generating model access status based on model endpoint reachability and credential referencing status; and generating recovery strategies based on credential rebinding requirements.

4. The method according to claim 1, characterized in that, The deployment policy package includes: converting the public network egress status to remote tool switch; limiting the allowed protocol set to local standard input / output protocols when the private network requirement is true or the public network egress status is closed; converting the original content saving restriction to a retrieval policy that only saves reference identifiers; converting the role-based permission model to a retrieval policy that requires permission context; and converting the credential rebinding requirement to a recovery policy field.

5. The method according to claim 1, characterized in that, The standardized encoding includes sorting field names, using compact delimiters, uniformly using UTF-8 character encoding, and rejecting non-finite values; each partition summary is calculated based on the evidence package identifier, running identifier, partition type, and corresponding partition content. The integrity of the file containing the deployed evidence data or the frozen archive is further verified by the file summary or archive summary, which is not used as the top-level field of the deployed evidence data.

6. The method according to claim 1, characterized in that, The retrieval enhancement strategy requires each retrieval reference record to include at least a data source identifier, a content fragment identifier, a document version, and a non-empty permission context; when the role-based permission model requires a permission context and any retrieval reference record lacks the permission context, the corresponding permission verification result will be set to failure.

7. The method according to claim 1, characterized in that, The tool call record includes at least a tool identifier, a side effect flag, an idempotent key, and a replay allowed flag. The tool call strategy includes a flag that prohibits non-idempotent automatic replay and an idempotent key requirement. For tool calls that have external side effects, allow replay, and lack an idempotent key, the tool replay condition check is set to fail and rollback blocking information is output.

8. The method according to claim 1, characterized in that, The preset gate rules include at least data structure verification, digest consistency verification, permission context existence verification, sensitive information verification, policy consistency verification, acceptance indicator verification, and rollback preparation condition verification; and output one of the following deployment verification states according to a fixed deterministic priority: acceptance failure, rollback required, external condition blocking, manual approval required, or available for private review.

9. The method according to claim 1, characterized in that, The rollback pointer consists of a rollback identifier and a target deployment version summary. A rollback plan is generated based on the rollback pointer and the evidence package identifier of the deployment evidence data. The rollback plan includes a target evidence package identifier, a state recovery pointer, a tool replay strategy, and minimum acceptance requirements. The state recovery pointer includes a state snapshot location and a state snapshot summary. The tool replay strategy includes a flag prohibiting non-idempotent automatic replay and an idempotent key requirement. The rollback exercise generates an exercise report by verifying the existence of the above fields, freezing archive integrity, and tool replay restrictions, without concluding that the target deployment version or state snapshot has been actually restored.

10. A smart agent application deployment verification and rollback control system, characterized in that, include: The environmental constraint acquisition module is used to acquire environmental constraint data of the target deployment environment; The deployment capability profiling module is used to convert the environmental constraint data into a deployment capability profile according to preset field mapping rules. The strategy generation module is used to generate a deployment strategy package based on the deployment capability profile. The acceptance execution module is used to execute deployment acceptance tasks according to the deployment strategy package and collect acceptance records. The evidence data generation module is used to perform standardized encoding and hash calculation on each record partition of the acceptance record to generate a partition summary, and to assemble the gate input and rollback pointer to generate deployment evidence data; The gate recalculation module is used to recalculate the deployment evidence data according to preset gate rules and output the deployment verification status; The rollback preparation control module is used to generate a rollback plan based on the rollback pointer and the evidence package identifier of the deployment evidence data when the deployment verification state indicates that a rollback is required, verify the state recovery pointer and tool replay strategy in the rollback plan, generate a drill report, and maintain the rollback required state and output rollback blocking information when the rollback preparation conditions are not met.

11. An electronic device comprising a processor and a memory, the memory storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method of any one of claims 1 to 9.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 9.

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