> For the complete documentation index, see [llms.txt](https://docs.nexthink.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.nexthink.com/user-guide/spark/working-with-spark.md).
# Working with Spark
Spark is a conversational IT agent that serves as the first point of contact for employees when they need IT assistance.
Unlike traditional chatbots that rely primarily on predefined scripts or static knowledge bases, Spark follows an agentic approach. It can reason over employee context and execute IT-approved actions to diagnose and remediate issues.
Spark leverages extensive device context and Digital Employee Experience (DEX) data, enabling more accurate analysis and targeted resolution. By combining contextual awareness, action capabilities, and learning from resolved incidents, Spark accelerates issue resolution and reduces the need for manual IT intervention.
## Spark architecture
**Spark** connects with employee requests through the communication channel, runs a diagnosis, and attempts issue resolution. The image shows a system architecture diagram that illustrates how Spark, an AI-powered support system, connects various components and handles employee requests end-to-end.
## Context and data inputs
To provide relevant responses, Spark uses a combination of static and dynamic data sources:
* **Contextual Nexthink data and capabilities**: Spark has access to the data available through the standard NQL data model. This includes device health, diagnostics, user metadata, remediations, Adopt guides, and newly released capabilities. Spark also has access to most of the dynamic data model, such as custom fields and data generated by remote action executions. Spark only accesses data relevant to the employee it is interacting with.
* **Knowledge base articles**: Knowledge base articles [manually imported](/user-guide/administration/ai-resources/managing-knowledge-sources.md) from the ITSM or ingested via the [ServiceNow Knowledge Base connector](/configuring_nexthink/bringing-data-into-your-nexthink-instance/integrating-nexthink-with-third-party-tools/inbound-connectors/connector-for-servicenow-knowledge-base.md).
* **Service request catalog**: Catalog structure, forms, and request metadata ingested from ServiceNow, which Spark can use to identify and recommend service requests and provide associated guidelines. Refer to [ServiceNow Request Catalog connector](/configuring_nexthink/bringing-data-into-your-nexthink-instance/integrating-nexthink-with-third-party-tools/inbound-connectors/connector-for-servicenow-request-catalog.md) for more information.
* **Local endpoint data**: Real-time data retrieved from the user’s device via the Nexthink Collector to support accurate, context-aware troubleshooting.
* **Past ticket resolution data:** Resolution notes for incidents resolved by support agents. Spark can use them to suggest remediations and continuously reduce manual interventions and escalations. Refer to [ServiceNow Tickets connector](/configuring_nexthink/bringing-data-into-your-nexthink-instance/integrating-nexthink-with-third-party-tools/inbound-connectors/servicenow-tickets-connector.md) for more information.
* **Curated list of trusted websites**: If enabled, Spark can search a curated list of trusted web domains when internal knowledge sources do not provide sufficient guidance. This capability helps Spark resolve a broader range of issues autonomously. Refet to [Managing Spark settings and data inputs](/user-guide/spark/setting-up-and-managing-spark/managing-spark-settings.md#enabling-web-search-in-spark) for more information.
Consequently, Spark relies on [specific NQL data model tables](/user-guide/spark/spark-nql-capabilities.md) to query Spark-user interaction data.
{% hint style="info" %}
Personal data handling is covered under the Nexthink Data Processing Agreement (DPA). Spark processing is user-specific and restricted to the customer region.
Spark never provides data from other organizations.
{% endhint %}
## Spark Actions
Spark can perform actions to diagnose and remediate employee issues. These actions are enabled by Nexthink administrators after IT approval. Before executing a remediation action, Spark requests confirmation from the employee. Built-in diagnostic actions are excluded from this requirement and may run in the background without interrupting employee work.
Refer to [Managing Spark actions](/user-guide/spark/setting-up-and-managing-spark/managing-agent-actions.md) for more information.
***
RELATED PAGES:
* [Understanding employee experience with Spark](/user-guide/spark/working-with-spark/understanding-employee-experience-with-spark.md)
* [Monitoring Spark](/user-guide/spark/working-with-spark/monitoring-spark.md)
* [Setting up and managing Spark](/user-guide/spark/setting-up-and-managing-spark.md)
* [Using Recommendations to enhance Spark knowledge and actions](/user-guide/spark/working-with-spark/using-recommendations-to-enhance-spark-knowledge-and-actions.md)
---
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