# Getting Started

AI Assets is the discovery and management layer for all AI components inside your enterprise.\
It extends the Platform by allowing you to connect environments and run scans to discover **Models, AI Workflows and MCP Servers** within your infrastructure.

To begin gaining insights in AI Assets:

1. [**Integrate your first environment**](/ai-asset-management/connect-environments.md) \
   **-** Connect your organization and repositories on GitLab or GitHub. 
2. [**Scan for AI Assets (Models, AI Workflow, MCP Servers)**](/ai-asset-management/scan-for-ai-assets.md) \
   **-** Scan your environments to identify models, complex AI workflows and MCP servers that populate your AI inventory with the results.
3. Results are available directly in:\
   \- [**Models**](/ai-asset-management/models.md) - shows models and automatically links them to our [**AI Benchmarks**](/ai-benchmarks/model-benchmarks.md), providing security, safety, and business alignment scores. This adds actionable context to the model inventory.\
   \- [**AI Workflows**](/ai-asset-management/ai-workflows.md) - shows complex workflows and maps every node, agent, and tool within them, generating a visual graph of how components interact within the system. Beyond static inventories, AI Workflows also performs threat analysis, detecting vulnerabilities at both the agent and tool level.\
   \- [**MCP Servers**](/ai-asset-management/mcp-servers.md) - provides a detailed view of MCP Servers used in connected environments, their tools, prompts, resources and resource templates.
4. Check and resolve any  [**AI Assets Issues**](/ai-asset-management/issues.md) automatically detected for you. Issues are generated based on predefined risk assessment criteria.

This inventory provides a live map of where AI components are located and how they interact, enabling enterprises to understand architectures, dependencies, and risks at scale.


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.probe.splx.ai/ai-asset-management/getting-started.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.