開発者向けドキュメント > Agent Assist > Workflows and prompting

Workflows and prompting¶

This page describes the interactive session: the welcome screen, slash commands, and the three main workflows (Design, Code, Deploy), plus prompting tips.

Start Agent Assist¶

空のディレクトリでdr assistを実行する

dr assistは、専用の空のディレクトリからのみ実行します。コードやその他のファイルが含まれるディレクトリでこのコマンドを実行するのは安全ではありません。エージェントアシストのコーディングワークフローを使用すると、アシスタントはDataRobot Agent Application Templateのクローンを現在のディレクトリに作成します。この操作によって既存のファイルが上書きされたり競合が発生したりする可能性があり、既存のプロジェクトが損なわれ、アシスタントの出力精度が低下する恐れがあります。 dr assistを実行する前に、専用のディレクトリがない場合は、ディレクトリを作成してそこでターミナルを開きます（たとえば、mkdir my-agent && cd my-agentを実行してから、dr assistを実行します）。

To begin, run the dr assist command:

dr assist

When DataRobot Agent Assist is started, the welcome screen shows the welcome message, three options, and the help footer:

Output: Welcome screen

$ dr assist
🔌 Running plugin: assist
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         █████             ████████    ████████  ████ ████████  ██     ███   █████    ████████     █████    ████
█████████

Welcome! I help you design, code, and deploy AI agents.

What would you like to do?
  1. Design an AI agent     → Describe your idea
  2. Code an AI agent       → Load and implement an existing agent_spec.md
  3. Deploy an AI agent     → Deploy an implemented agent to DataRobot

Type /help for commands | /quit to exit

Input is collected at the $ prompt. Type a number (1, 2, or 3) to choose a category, or, describe the goal in natural language. If the first instruction is one of those numbers, the assistant treats it as referring to the predefined categories.

Slash commands

Slash commands use the / prefix and are built in. For the full list and descriptions, see the Environment and commands reference. Typing / shows the list of available commands and suggests typing /help for details. The /quit command exits the session.

Design an AI agent¶

Use this workflow when starting a new agent idea. The assistant asks clarifying questions about the agent’s purpose, capabilities, and requirements; suggests tools and models; and produces an agent specification. The specification is displayed and written to agent_spec.md in the current directory (YAML format). The specification can be refined over several iterations. The assistant is instructed to limit clarifying questions to about two rounds before proposing an initial draft, then iterate on that draft. Once you have a spec, you can simulate the agent to validate the design before coding.

Example conversation: Design a customer support agent

$ I want to create a customer support agent for an e-commerce site
Agent Assist: I'll help you design that! I can ask some questions to understand your requirements better. What specific customer issues should this agent handle?
User: $ It should handle order status, returns, and product questions
DataRobot Agent Assist: Great! What data sources will it need access to?
User: $ Our order database and product catalog
DataRobot Agent Assist: Perfect. Should it escalate complex issues to humans?
User: $ Yes, escalate billing and refund requests
DataRobot Agent Assist: I'll create a specification for your customer support agent...
[Creates and displays agent_spec.md, saves to file]

Simulate an agent¶

After you have an agent_spec.md, you can simulate the agent before implementing it. Simulation is for design validation only: no real tool code runs, and no external services are called.

What happens: The model (LLM) uses your spec to decide which tools to call and with what arguments. When the model requests a tool call, Agent Assist does not run your tool implementation or call DataRobot deployments, datasets, or other APIs. Instead, it uses the LLM to generate simulated return values that match the tool’s described inputs and outputs. You see realistic-looking tool inputs and outputs so you can judge whether the design makes sense.
What it’s for: Validate that the right tools exist, that input/output structures are reasonable, and that the selected model tends to call the right tools for the task—before you spend time implementing real tool code.
What it’s not: Simulation does not call real DataRobot deployments, read real datasets, or hit real APIs. To use live data and deployments, implement the agent and run it in the Code or Deploy workflow.

Ask the assistant to simulate your agent (for example, “Simulate my agent” or “Let me try the design”) to begin. Type /quit to exit the simulation.

Code an AI agent¶

Use this workflow when the agent_spec.md file already exists and is ready to implement. The assistant loads the specification and—if the template repository is not yet present—uses the prepare_to_code tool to clone the DataRobot Agentic Starter repository. It then follows the template’s AGENTS.md for setup and implementation. The assistant can run shell commands (with approval), edit files, and manage multi-step work with tools. It suggests testing locally and deploying when the implementation is ready.

Example conversation: Add a tool to an existing specification

User: $ Load my existing agent specification and add a tool for inventory checking
Agent Assist: I found your agent_spec.md. I can see it's a customer support agent.
Let me add an inventory checking tool...
[Reads current spec and adds the requested tool]

Deploy an AI agent¶

Use this workflow when an implemented agent is ready to deploy to DataRobot. The assistant reads the deployment instructions from the template’s AGENTS.md and guides users through the steps. It does not run deployment commands; it indicates what to run (in a separate terminal). For interactive or long-running steps (such as dr start), the assistant uses the appropriate tools as defined in the codebase.

Example conversation: Deploy the agent

User: $ Help me deploy the agent I implemented
Agent Assist: I'll read the deployment section of AGENTS.md and walk you through the steps...
[Displays deployment commands and instructions]

Interact with the DataRobot CLI¶

DataRobot Agent Assist runs certain DataRobot CLI (dr) commands on your behalf and can run other shell commands (including additional dr commands) with your approval.

At startup: The assistant runs dr dependencies check to verify that required tools (Python, Node, Git, Task, Pulumi, etc.) are installed and meet minimum versions. See Prerequisites and installation.
Code workflow: When you use the assistant to clone and prepare the template (for example, "Code an AI agent" or "prepare to code"), it runs dr start in the template directory to initialize the DataRobot project. You are prompted to approve before the command runs.
Any workflow: The assistant can run shell commands—including dr subcommands—after you approve. It shows the command and description, then prompts for approval before executing.

For full DataRobot CLI usage (authorization, deployment, and other commands), see the DataRobot CLI documentation.

Prompting considerations¶

Consider the following guidelines to get better results from the assistant:

Be specific: "Create a customer support agent that handles order inquiries and escalates billing issues" rather than "Create an agent".
Provide context: "I'm building a travel booking agent and need help with the flight search API integration" rather than "Help me with my code".
Ask for explanations: "Explain why this code isn't working and show me how to fix it" rather than "Fix this".
Iterate: "This is good, but can you make it handle edge cases like cancelled orders?" rather than accepting the first solution.