Agents

​

Creating an agent

• From a template
• Blank agent
• Anaconda Assistant

1. Select Agents from the left-hand navigation.
2. Click Create.
3. Select a template to create the agent.

1. Select Agents from the left-hand navigation.
2. Click Create.
3. Click Blank agent at the bottom.
   
4. Enter a Name for your agent.
5. Select an AI Provider and Model.
6. Click Create Agent.

The Anaconda Assistant is available from the Home page, or by selecting in the upper-right corner of any page.Describe the agent you want to create and the assistant handles the rest. Detailed requests that include your agent’s purpose and intended behavior can result in a fully configured agent, including its system prompt, AI provider, and tools.

​

Configuring your agent

The agent configuration page displays all components that define your agent’s behavior. Select an agent’s card from the Agents page to view its configuration.

​

AI provider

The AI Provider row configures which model powers your agent and how it generates responses.

1. Select an AI provider from the Provider dropdown. For more information, see AI providers.
2. Select a model from the Model dropdown.
3. Click the settings icon beside the Model dropdown to open the Model Settings dialog and adjust the following configurations as needed:
   • Max tokens: The maximum number of tokens the model can generate in a single response. Leave blank to use the model’s maximum output length.
   • Output format: Choose between Text (default) and JSON for structured output.
   • Temperature: Controls randomness in responses. Ranges from 0 (most deterministic) to 2 (most creative).
     Lower values work well for analytical tasks; higher values suit more creative tasks.
4. Click Done.
   

​

Sandbox

1. Turn on the Sandbox toggle.
2. Click the settings icon beside the Sandbox toggle to open the Sandbox Settings dialog and adjust the following configurations as needed.
3. Select a network mode from the Network dropdown:
   • Outbound (default): The agent can make outgoing network requests. Agent Studio publishes the container’s port to localhost so you can chat with it.
   • Isolated: No network access. The agent cannot reach external services or be reached from the host.
   • Host network: The agent shares the host’s network namespace directly.
4. (Optional) Set CPUs and Memory limits for the container.
5. (Optional) Add folder mounts to give the agent access to specific directories on your machine. Click Add mount, then configure:
   • Host path: The directory on your machine to share with the container. Click the folder icon to browse your file system.
   • Sandbox path: The location inside the container where the directory is mounted.
   • Permissions: Choose R (Read-only) or RW (Read/write).
   To remove a folder mount, click Remove.
6. Repeat the previous step to add as many folder mounts as your agent requires.
7. Click Done.

​

Python tools

​

Creating a Python tool file

1. Click Add beside Python Tools.
2. In the Create File dialog, enter a name using letters, numbers, and underscores. The name must start with a letter or underscore.
3. Click Create. Agent Studio opens your default editor and creates the file with a sample tool function:
   """my_tool tool module."""
   
   from anaconda_agent_runtime.tools import tool
   
   
   @tool
   def my_tool(query: str) -> str:
       """Describe what this tool does.
   
       Args:
           query: The input to process.
   
       Returns:
           The result of the operation.
       """
       return f"Result for: {query}"
   

   See all 16 lines
4. Customize the generated function with your tool’s logic.
5. Save the file.
   The file uses the function name as the tool name; argument names and type hints to build the tool’s input schema; and the docstring as the tool’s description.Write a clear docstring that describes what the tool does, what it accepts, and what it returns.

To remove a Python tool, click , then click Delete to delete the file.

​

Adding dependencies

[dependencies]
numpy = "*"          # conda

[pypi-dependencies]
requests = "*"       # PyPI

​

Tool approval controls

• Default Permissions: Controls the approval behavior for all enabled write tools in the file.
• Write Tools: Lists the individual @tool functions in the file. Turn on the toggle beside each function to enable it as a write tool.

1. Click beside the tool file’s name, then click Edit Tool Policy.
2. Under Security, select a Default Permissions setting:
   • Always Allow: The agent can call enabled write tools without asking for permission.
   • Always Ask: The agent requests approval before calling any enabled write tool.
   • Custom: Set a permission for each write tool individually.
3. Under Write Tools, turn on the toggle beside each @tool function you want to enable as a write tool.
   

​

Plugins

• Anaconda: Connect to conda environment and package management capabilities and Anaconda documentation.
• nteract: Work with Jupyter notebooks through MCP, including creating, running, and managing cells and kernels.
• Jupyter: Inspect notebook files, list kernels, connect to notebooks, run notebook workflows, and work with Jupyter context.
• Atlassian: Access Jira and Confluence for ticket management, sprint tracking, and documentation.
• Miro: Create and edit content on Miro boards, including sticky notes, flows, and diagrams.
• Omni Analytics: Query data using natural language through Omni’s governed semantic model.
• Ramp: Enable secure, read-only access to Ramp data for customer-defined logic.
• GitHub: Create and review pull requests, manage issues, trigger and monitor Actions runs, clone repos, and more.
• Web Search: Query the web to return titles, URLs, and text content.

1. Click Add on the Plugins row.
2. Select a plugin from the available options.
3. Click Add to agent.

To remove a plugin, click beside the plugin, then click Remove.

​

Skills

1. Click Add on the Skills row.
2. Click Add to open the Add Skill dialog.
3. Choose a source and configure the skill:
   • GitHub
   • Local folder

   Add a skill from a public GitHub repository.

   1. Enter the repository Owner and Repository name.
   2. (Optional) Enter a Skill name to add a specific skill from the repository. Leave this field blank to add all skills in the repository.
   3. Click Add.

   

   Add a skill from a folder on your machine.

   1. Click Browse and select a folder. The folder must contain a SKILL.md file, or be a parent folder of skill subfolders to install them all.
   2. Click Add.
4. Click Add to Agent.

To remove a skill, click beside the skill, then click Remove.

​

Files

To remove a file, click beside the file name, then click Remove.

​

Instructions

​

Starting and chatting with your agent

1. From the agent configuration page, click Start.
2. Wait for the agent to finish starting. A notification appears when the agent is ready.
3. Click Chat to open a conversation with the agent.

To stop a running agent, click Stop from the agent configuration page.

You can also click Chat on a stopped agent. The agent starts automatically when you send your first message.

​

Monitoring agents

​

Activity

• From the navigation menu
• From a chat

Click Activity to open the monitor page. This view shows events from all running agents.

Use the controls in the upper-right corner to change the view:

• Timeline: Displays events in chronological order across all agents.
• By Agent: Groups events under each agent.
• Reset: Clears all recorded events from the monitor.
• Open in new window: Opens the monitor page in a dedicated window.

Click Activity in the chat window to open a panel on the right side. This view shows events for the current conversation.

​

Usage

Click Usage from the left-hand navigation to open the page.

• Today: Usage from the current day.
• 7 Days: Rolling 7-day window.
• 30 Days: Rolling 30-day window.
• All Time: All recorded usage.
• Refresh: Updates the data to include the most recent usage.

• Requests: Total number of model requests sent.
• Tokens In: Total input tokens sent to the model.
• Tokens Out: Total output tokens returned by the model.
• Estimated Cost: Approximate cost based on public pricing for the models used.

​

Comparing agents and models

1. Select Chats from the left-hand navigation.
2. Click New, then click Compare Chat.
3. Select the checkbox beside two to four agents or models to compare.
4. Click Start.
5. Enter a message. Agent Studio sends it to all selected agents or models simultaneously and displays their responses in side-by-side columns.

​

Connecting to your agent

Every running agent exposes connection options for external tools, browser-based chat, and direct HTTP access. Once your agent is running, click Connect from the agent configuration page to open the Connect dialog.

​

MCP server

​

Web chat

Click Open to open a browser-based chat interface for the agent, or Copy URL to share or bookmark the link.

​

Advanced endpoints

​

Opening agent files

Click in the upper-right corner of the agent’s page, then click Open to open your agent’s project folder in your file system or an external editor like VS Code. The folder contains the agent definition (agent.yaml), Python tool modules, skills, and project configuration.

​

Multi-agent orchestration

1. Start the target agent.
2. Click Connect from the target agent’s configuration page.
3. Copy the streamable HTTP URL from the Custom tab in the Connect dialog.
4. In the calling agent’s configuration page, click Add on the Plugins row.
5. Click Add beside Custom.
6. Give the custom MCP a name and paste the target agent’s URL into the URL field.
7. Click Add to agent.

​

Deleting an agent

1. Select Agents from the left-hand navigation.
2. Click on the agent’s card.
3. Select Delete to remove the agent.