> For the complete documentation index, see [llms.txt](https://docs.tonic.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.tonic.ai/fabricate/databases-and-data/simulations/simulations-about.md).
# About simulations
A simulation is a workflow that generates a set of interrelated, time-based data that can include multiple types of content.
Simulation results essentially tell a story.
For example, you might create a simulation to replicate the assorted documents and communication associated with a development manager starting and tracking the progress of a development project. There might be Slack conversations, email messages, technical specification documents, and project plans.
Or you could produce a set of content that traces the diagnosis, treatment, and insurance reimbursement for a patient's illness. There might be appointment notes, email messages to request and grant authorization, treatment plans, and billing records.
## Components of a simulation
Every simulation starts with a scenario, which is a prompt that describes what the simulation is about and the types of events and actors that it includes. A scenario might be as simple as a single paragraph, or can be a longer, more detailed document.
Fabricate uses the scenario to create a project workflow that you then use to run the simulation and generate the results.
When Fabricate runs a complete simulation workflow, it generates the following:
Diagram that shows the content generated from a simulation
* **Prehistoric data -** Prehistoric data is a collection of generated artifacts of the same type as the simulation artifacts, to establish data that is in place before the simulation timeline starts.
* **Simulation timeline -** The sequence of events for the simulation. Similar to a theatrical script, a simulation timeline consists of acts and scenes.
* **Simulation cast -** The personas involved in the simulation. The simulation cast includes a main character, who provides the main point of view for the simulation.
* **Simulation artifacts -** The content generated from the timeline. The simulation artifacts consist of content in one or more of the available content platforms. Examples of simulation artifacts include email messages, Slack messages, documents, spreadsheets, and presentations.
## Available content platforms for simulation artifacts
* The simulation artifacts can be from any of the following content platforms:
* Affinity
* Atlassian - Includes Jira and Confluence
* Databricks
* Egnyte
* GitHub
* Gong
* Google Workspace - Includes Gmail, Calendar, Drive, Docs, Sheets, and Slides
* Microsoft 365 - Includes Word, Excel, and PowerPoint
* Microsoft Outlook - Includes mail and calendar
* Notion
* Pipedrive
* Salesforce
* Sentry
* ServiceNow
* Slack
* Zoom
For each content platform, the [simulation results](/fabricate/databases-and-data/simulations/simulation-explore-results.md#using-the-content-platform-simulators) provide a UI simulator. For example, you view generated Slack messages in a realistic simulation of the Slack user interface.
To allow agents to interact with the results for individual products, Fabricate provides a set of [built-in MCP servers](/fabricate/databases-and-data/simulations/simulation-mcp-servers.md).
---
# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.
## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.
Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:
```
GET https://docs.tonic.ai/fabricate/databases-and-data/simulations/simulations-about.md?ask=&goal=
```
`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.
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.
