Creating Custom Agents

In addition to the built-in K2View agents, Studio AI lets you create your own agents. A custom agent has a name, a focused purpose, and a prompt you write. Once created, it appears in the chat alongside the built-in agents and can be addressed with @AgentName like any other.

Custom agents are useful when you have a repeated workflow that none of the built-in agents covers well — for example, an agent specialized in a specific LU, a coding style enforcer, or an agent that always injects a particular set of project context.

Creating a Custom Agent

1. Open the AI Configuration panel (click the configuration icon in the AI Chat panel bar, or go to View > AI Configuration).
2. Select the Agents tab.
3. Click Add Custom Agent at the bottom of the agent list.
4. Fill in the agent definition fields (described below).
5. Click Save.

The new agent is immediately available in the AI Chat.

Agent Definition Fields

Global vs Workspace-Specific Agents

Custom agents can be stored in two locations:

Global agents are stored in your user-level configuration directory (~/.theia/). They are available in every workspace you open on your machine.

Workspace-specific agents are stored in the .theia/ directory of a specific project. They are only available when that project is open, and they take priority over global agents with the same ID. This is useful for project-specific assistants — for example, an agent pre-loaded with context about a particular Logical Unit.

When you create an agent via the UI, Studio AI prompts you to choose whether to save it globally or for the current workspace.

Writing an Effective Custom Agent Prompt

The prompt is the most important part of a custom agent. A few guidelines:

Start with identity and scope. Tell the agent what it is and what it should focus on:

You are a specialized assistant for the customer_bank Logical Unit in this K2View project.
You help with designing and modifying tables, populations, enrichment functions, and globals
within this LU only.

Include project-specific conventions. If the agent should follow particular patterns:

All enrichment functions must follow the pattern established in
~{readFile('implementation/Shared Objects/functions/ParseDate.java')}.
Use try-with-resources for all database operations.

Set boundaries. Tell the agent what it should not do:

Do not modify any files outside the customer_bank LU directory. If a request requires
cross-LU changes, describe what is needed and ask the user to confirm.

Use variables for dynamic context. Inject the current file or project path automatically:

The project is located at {{projectRoot}}.
When the user asks about the current file, refer to {{currentRelativeFilePath}}.

Example: A LU-Specific Coding Agent

Here is a complete example of a custom agent focused on a specific Logical Unit:

---
name: CustomerBank-Dev
description: Coding assistant specialized in the customer_bank Logical Unit
---
You are a senior K2View developer specializing in the customer_bank Logical Unit.

Your responsibilities:
- Writing and modifying Java functions (enrichments, populations, decision functions)
- Designing LU table schemas
- Reviewing code for correctness and K2View best practices

Conventions to follow in this project:
~{fragment('shared/coding-standards')}

Current project: {{projectRoot}}
Current file: {{currentRelativeFilePath}}

Only modify files within the customer_bank LU. For cross-LU work, propose the changes
and ask the user to confirm before proceeding.

Editing and Deleting Custom Agents

To edit a custom agent, open AI Configuration, find the agent in the Agents tab, and click Edit. You can modify any field and save.

To delete a custom agent, click Delete next to the agent in the list. Built-in agents cannot be deleted (only disabled).