Agentic systems and methods for managing patch deployment

1 . A computer-implemented method of managing deployment of a software patch to one or more client devices, comprising:

operating an agentic system configured to operate over time without human intervention;

autonomously monitoring, by the agentic system, one or more patch-related information sources to identify availability of a software patch and associated patch identifiers;

autonomously retrieving, by the agentic system, community-sourced patch data from a plurality of third-party information sources external to a patch vendor and a patch management platform;

determining, by the agentic system, from the retrieved community-sourced patch data, a volume-related characteristic of patch-related findings across the third-party information sources and a credibility-related characteristic representing a trustworthiness of at least a portion of the third-party information sources;

evaluating the retrieved community-sourced patch data together with vendor-supplied patch data and platform-based data originating from the one or more client devices;

generating, using at least the volume-related characteristic and the credibility-related characteristic, a bounded risk representation of deploying the software patch on at least a subset of the one or more client devices; and

executing a deployment action for the software patch based on the bounded risk representation, the deployment action controlling whether, when, or how the software patch is deployed to the one or more client devices.

2 . The method of claim 1 , wherein generating the bounded risk representation comprises:

applying at least one trained machine-learning model having a plurality of internal numerical parameters learned during training from historical software patch deployment outcomes;

wherein the internal numerical parameters are altered during training based on comparisons between predicted deployment outcomes and observed deployment outcomes;

wherein the internal numerical parameters learned during training are not explicitly specified by a human operator and are not practically determinable by mental processes; and

wherein the bounded risk representation is generated using the internal numerical parameters learned during training to control deployment of the software patch to the one or more client devices.

3 . The method of claim 2 , wherein the internal numerical parameters learned during training include:

parameters that control weighting of relevance-related feature values based on similarity between attributes of patch-related information and configuration attributes of the one or more client devices;

parameters that control weighting of severity-related feature values; and

parameters that control weighting of consistency-related feature values representing correlation or similarity across multiple independent sources of community-sourced patch data.

4 . The method of claim 1 , wherein determining the volume-related characteristic comprises aggregating a quantity, frequency, or rate of occurrence of patch-related findings represented in the community-sourced patch data across the plurality of third-party information sources within a defined time interval.

5 . The method of claim 1 , wherein determining the credibility-related characteristic comprises:

applying at least one trained machine-learning model having internal numerical parameters learned from historical accuracy of third-party information sources represented in the community-sourced patch data relative to observed software patch deployment outcomes;

wherein the internal numerical parameters are altered during training based on comparisons between predicted deployment reliability and observed deployment outcomes; and

wherein the learned internal numerical parameters are not explicitly determined by a human operator.

6 . The method of claim 1 , wherein autonomously monitoring comprises monitoring the one or more patch-related information sources according to a defined time interval to detect availability of the software patch.

7 . The method of claim 1 , wherein autonomously monitoring comprises detecting a change in content of at least one patch-related information source indicative of availability of the software patch.

8 . The method of claim 1 , wherein the third-party information sources include at least one of online discussion forums, third-party security advisories, technical blogs, crowd-sourced error repositories, and social media platforms from which the community-sourced patch data is obtained.

9 . The method of claim 1 , wherein executing the deployment action includes at least one of:

deploying the software patch to at least a first subset of the client devices;

deferring deployment of the software patch;

withholding deployment of the software patch; or

generating a notification to a technician computer system.

10 . The method of claim 1 , wherein the platform-based data includes telemetry or performance data from one or more client devices acquired via a computer network.

11 . The method of claim 1 , further including acquiring the vendor-supplied patch data by accessing, via a computer network, a vendor distribution service to retrieve patch information associated with the software patch.

12 . The method of claim 1 , further comprising:

monitoring post-deployment platform-based data for the one or more client devices; and

initiating a rollback of the software patch on at least a subset of the one or more client devices when one or more anomaly metrics satisfy a predefined threshold.

13 . The method of claim 1 , further comprising:

generating, based at least in part on the bounded risk representation and device classification information associated with the one or more client devices, a plurality of deployment rings defining different subsets of the one or more client devices and an order in which the software patch is to be deployed to the different subsets; and

deploying the software patch to the plurality of deployment rings in accordance with the order.

14 . The method of claim 13 , further comprising dynamically recalculating at least one of the plurality of deployment rings in response to updated platform-based data indicating at least one of performance degradation, anomaly rates satisfying a threshold, or stability across one or more of the deployment rings.

15 . The method of claim 1 , wherein the agentic system modifies retrieval of the community-sourced patch data over time based on relevance or usefulness of previously retrieved community-sourced patch data, and wherein the credibility-related characteristic is updated by at least one trained machine-learning model that updates internal numerical parameters based on deployment outcomes observed after execution of the deployment action.

16 . A system for managing deployment of a software patch to one or more client devices, comprising:

one or more processors; and

a memory storing instructions that, when executed by the one or more processors, cause the system to:

provide an agentic system configured to operate over time without human intervention;

autonomously monitor, by the agentic system, one or more patch-related information sources to identify availability of a software patch and associated patch identifiers;

autonomously retrieve, by the agentic system, community-sourced patch data from a plurality of third-party information sources external to a patch vendor and a patch management platform;

determine, by the agentic system, from the retrieved community-sourced patch data, a volume-related characteristic of patch-related findings across the third-party information sources and a credibility-related characteristic representing a trustworthiness of at least a portion of the third-party information sources;

evaluate the retrieved community-sourced patch data together with vendor-supplied patch data and platform-based data originating from the one or more client devices;

generate, using at least the volume-related characteristic and the credibility-related characteristic, a bounded risk representation of deploying the software patch on at least a subset of the one or more client devices; and

execute a deployment action for the software patch based on the bounded risk representation, the deployment action controlling whether, when, or how the software patch is deployed to the one or more client devices.

17 . The system of claim 16 , wherein the agentic system retrieves the community-sourced patch data in an iterative manner over time and modifies retrieval behavior based on relevance or usefulness of previously retrieved community-sourced patch data associated with the software patch.

18 . The system of claim 16 , wherein the agentic system includes at least one trained machine-learning model having internal numerical parameters learned from historical accuracy of third-party information sources represented in the community-sourced patch data relative to observed software patch deployment outcomes, and wherein the internal numerical parameters are updated over time based on deployment outcomes resulting from execution of the deployment action.

19 . The system of claim 16 , wherein the agentic system autonomously monitors the one or more patch-related information sources according to a defined time interval to detect availability of the software patch.

20 . The system of claim 16 , wherein the agentic system determines the credibility-related characteristic by:

applying at least one trained machine-learning model having internal numerical parameters learned from historical accuracy of third-party information sources represented in the community-sourced patch data relative to observed software patch deployment outcomes;

wherein the internal numerical parameters are altered during training based on comparisons between predicted deployment reliability and observed deployment outcomes; and

wherein the learned internal numerical parameters are not explicitly determined by a human operator.