--- sourceDocument: Australia Enable AI sourceDocumentLink: https://www.servicenow.com/docs/r/australia/intelligent-experiences Release : - australia ft:locale : - en-US ft:publication_title : - Australia Enable AI ft:clusterId : - platai bundleId : - platai workflow : - Platform --- # Pattern Diagnostic AI agent # Pattern Diagnostic AI agent {#ariaid-title1} Release version: Australia Updated October 6, 2026 ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 2 minutes to read Summarize ![AI sparkle icon](https://servicenow.com/docs/portal-asset/ai-sparkle-icon) Summarized using AI This content was generated using new OpenAI-powered functionality. Results are provided on an as is basis and are not guaranteed to be accurate or complete. ## Summary of Pattern Diagnostic AI agent The Pattern Diagnostic AI agent is designed to enhance the quality of Configuration Management Database (CMDB) data by analyzing Discovery pattern metadata. It helps ServiceNow customers identify and diagnose missing attribute values in recently discovered configuration items (CIs), streamlining issue resolution by leveraging discovery logs and the Error Framework. Show full answer Show less ## Key Features * **Attribute and CI Class Identification:** The agent assists users in selecting the CI class and attribute to investigate, suggesting options based on Discovery pattern data. * **Verification of Attribute Population:** Confirms whether Discovery is responsible for populating the attribute or if it requires manual or external input. * **CI Data Analysis:** Searches for CIs discovered within the last 30 days missing the specified attribute value and checks for affected records. * **Discovery Log Parsing:** Resolves managed IP addresses and analyzes discovery logs for errors, warnings, and diagnostic messages explaining missing data. * **Error Classification and Logging:** Classifies primary errors from logs and logs them to the Error Framework when matching error codes are found. * **Investigation Summary:** Provides a comprehensive summary covering responsible Discovery patterns, affected CIs, and likely root causes, including links to CI records and logs. * **User Interaction:** Allows users to investigate different attributes or CI classes iteratively. * **Configuration Options:** Includes settings to enable third-party or AI specialist access, manage long-term memory for context, and specify allowed user roles and data access roles (primarily discoveryadmin). * **Integration with Tools:** Utilizes specialized tools such as Discovery Log Parser, Error Classifier, ManagedIP Resolver, Missing Data Detector, and Pattern Lookup to perform diagnostics. * **Workflow Integration:** Compatible with Pattern Diagnostic Workflow and configurable for Virtual Agent or ServiceNow Otto interfaces. ## Practical Benefits for ServiceNow Customers * Quickly identify why Discovery failed to populate specific CI attributes, improving CMDB data completeness and accuracy. * Gain actionable insights into discovery failures through automated log parsing and error classification. * Streamline remediation by automatically logging errors into the Error Framework for further handling. * Empower administrators with intuitive investigative workflows and detailed summaries linking to relevant records and logs. * Customize access and capabilities based on roles and enable specialist configurations for advanced diagnostics. The Pattern Diagnostic Agent AI agent analyzes CMDB configuration item (CI) data quality by using Discovery pattern metadata. The agent identifies CIs with missing attribute values, parses discovery logs to diagnose the failures, and prepares the error data for remediation through the Error Framework. ## Workflow {#pattern-diagnostic-ai-agent__xoonsykrlvun} The agent helps the user find out why Discovery didn't populate an attribute on recently discovered CIs of a specific CI class. 1. Identify the CI class and the attribute to investigate, suggesting matching CI classes and the attributes that Discovery patterns populate if the user doesn't specify them. 2. Check the Discovery pattern metadata to confirm that Discovery is responsible for populating the attribute, and inform the user if the attribute requires manual entry or an external integration instead. 3. Find the CIs discovered by ServiceNow in the last 30 days that are missing a value for the attribute, and report whether any affected CIs exist. 4. Resolve the managed IP addresses for the affected CIs and search the discovery logs for errors, warnings, and diagnostic messages that explain the missing data. 5. Classify the primary error from the discovery logs and log it to the Error Framework when a matching error code is found. 6. Present an investigation summary that covers the Discovery pattern responsible for the attribute, the affected CIs, and the likely root cause, with links to the CI records and discovery logs. 7. Ask the user whether they want to investigate a different attribute or CI class. {#pattern-diagnostic-ai-agent__ol_xdt_ldq_obr} {#pattern-diagnostic-ai-agent__xoonsykrlvun__entry__2} | Field | Description | |-|-| | Allow third party to access this AI agent | When enabled, third-party AI agents can use this agent. This value is off (false) by default. This setting is defined in the AI Agent configs \[sn_aia_agent_config\] table on the External discoverable field. | | Allow AI specialists to access this AI agent | When enabled, AI specialists can use this agent. This value is off (false) by default. When set to true, more configuration options for tools become available so that an AI specialist can map inputs and response templates to tool outputs. This setting is defined in the AI Agent configs \[sn_aia_agent_config\] table on the Specialist enabled field. | | Manage long-term memory | When enabled, all previous user interactions are used as context for the LLM. This value is off (false) by default. This setting is defined by the sn_aia.ltm.enable_long_term_memory system property. For more information, see [ServiceNow Otto AI agents reference](https://www.servicenow.com/docs/MUqSU8rHp3ao~q3QEWgHkA "Find more information about user roles, tables, and the different properties that are installed in ServiceNow Otto AI agents."). | | Tools | Script : DQA Discovery Log Parser : DQA Error Classifier : DQA ManagedIP Resolver : DQA Missing Data Detector : DQA Pattern Lookup : Resolve CI Class | | Allowed user roles The specific user roles that can access this AI agent. | discovery_admin | | Data access roles The specific user identity roles that determine which data the AI agent can access and what actions it can take. | discovery_admin | | Triggers | Optional. None defined by default. An admin can specify triggers if desired. For more information, see [Add a trigger to an AI agent](https://www.servicenow.com/docs/NXqRJBQV_Vw00FEzklcAJw "In the guided setup for an AI agent, add triggers to run the AI agent automatically when certain conditions are met."). | | Channels | Configure an assistant for Virtual Agent or ServiceNow Otto panel using [Assistant Designer](https://www.servicenow.com/docs/access?context=configure-now-assist-va&version=australia&pubname=australia-conversational-interfaces&ft:locale=en-US). | | Used in agentic workflows | Pattern Diagnostic Workflow | [Table 1. Configuration]