# Start with Agent Assist
URL: https://docs.datarobot.com/en/docs/get-started/day0/gs-agent-assist.html

> Agent Assist (dr-assist) is an interactive AI assistant that helps users design, code, and deploy AI agents through natural conversation.

# Start with Agent Assist

DataRobot Agent Assist ( `dr-assist`) is an interactive AI assistant optimized for the development of AI agents. It helps users design, code, and deploy agents through natural conversation—users describe the agent they want, and the assistant helps build it on the foundation provided by the [Agentic Starter application template](https://github.com/datarobot-community/datarobot-agent-application).

DataRobot Agent Assist integrates with the [DataRobot CLI](https://docs.datarobot.com/en/docs/agentic-ai/cli/index.html) as a plugin and uses the [DataRobot LLM gateway](https://docs.datarobot.com/en/docs/agentic-ai/genai-code/dr-llm-gateway.html) for model access. During the design and code cycle, Agent Assist can outline which tools an agent should call based on the proposed functionality—for straightforward tools, it can implement the tool code; for more complex tools (such as those that consume API tokens or write to a database), it can scaffold the initial file structure for the human-in-the-loop to complete in the editor or development environment of their choice.

> [!WARNING] Run dr assist in an empty directory
> Only run `dr assist` from a dedicated and empty directory. Running this command in a directory containing code or other files is unsafe. When you use the agent assist coding workflow, the assistant clones the DataRobot Agent Application Template repository into the current directory. This action can overwrite or conflict with existing files, damaging the existing project and degrading the accuracy of the assistant's output. Before running `dr assist`, if you're not in a dedicated directory, create one and open the terminal there (for example, `mkdir my-agent && cd my-agent`, then run `dr assist`).

DataRobot Agent Assist can:

- Design AI agents by helping users think through specifications, ask clarifying questions, and produce an agent specification file ( agent_spec.md ).
- Research solutions using file search and analysis (an internal agent can read files, list directories, grep, and glob).
- Code AI agents by loading an existing agent_spec.md , cloning the DataRobot agent template repository, and implementing the agent with file edits and shell commands.
- Simulate an agent from a specification before coding. In this simulation , the model chooses tools and arguments, but tool calls are not executed. Returns are generated by the LLM so you can validate design (which tools, I/O shapes, model behavior) without calling real deployments or datasets.
- Deploy agents to DataRobot following the template’s deployment instructions.

| Page | Description |
| --- | --- |
| Prerequisites and installation | System requirements, required tools and versions, installing the plugin or running standalone, verifying installation. |
| Workflows and prompting | Welcome screen, slash commands, Design / Code / Deploy workflows, prompting tips. |
| Environment and commands reference | Environment variables table, files and directories, slash commands. |
| Troubleshooting | Plugin not discovered, dependency check failed, authentication errors, template bootstrap, LLM API errors, session interruption, and related fixes. |

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