Agents

Agents are AI-powered assistants that can answer questions about your data, execute code, generate visualizations, and orchestrate multi-step workflows — all grounded in your verified enterprise data.

​

Configuration

​

Model Selection

Choose a completion model for your agent from the model dropdown. Corvic supports a range of models across speed and capability tiers: Fast Models:

• Google: Gemini 2.0 Flash
• Google: Gemini 2.5 Flash
• OpenAI: GPT 5 Mini
• OpenAI: GPT 5 Nano
• OpenAI: GPT 4.1 Mini
• OpenAI: GPT 4.1 Nano

Reasoning Models:

• Google: Gemini 2.5 Pro
• Google: Gemini 3.1 Pro
• OpenAI: GPT 5
• OpenAI: GPT 5.1
• OpenAI: GPT 5.2

Large Models:

• OpenAI: GPT 4.1

If you have custom LLM Endpoints configured in your organization, those models will also appear in this list. The Agent tab also shows the MCP Tools connected to your agent (such as web search, file access, and database queries) and any Corvic Apps linked to the agent for data pipeline access.

​

Skills, Artifacts & User Files

The agent sidebar provides quick access to key capabilities:

• Summary — A conversation summary that updates as you chat, with an audio summary option (coming soon)
• Skills — Reusable agent skills that extend what your agent can do. Add new skills by chatting with the agent itself, or use the ”+” button to upload new skills.
• Artifacts — Outputs generated during conversations (tables, charts, code) are collected here for easy access
• User Files — Upload documents directly into the chat for the agent to process and use as context

​

Agent Appearance

Customize how your agent looks and is described. Click on the agent name to open the Appearance settings, where you can configure:

• Name & Description — Give your agent a clear identity
• Quick Start Video URL — Link a video to help users get started
• App Documentation URL — Point users to relevant documentation
• Color & Icon — Choose a brand color and icon, or upload your own

​

Chatting

Ask your agent a question in the chat input. The agent will orchestrate a chain of actions — querying tables, executing Python code, generating visualizations — to deliver a context-aware response grounded in your data.

​

Explainability

​

Thought Process

Every agent response includes a transparent view of the reasoning chain. As the agent works, you can see each action it takes — listing tables, retrieving data, executing Python, generating plots — displayed as a step-by-step flow.

​

Action Output

Click on any action step to inspect its full output. For code execution steps, you can view the Python code that was run, the output variables, and the resulting data tables — giving you complete transparency into how the agent arrived at its answer.

​

Sharing & Integration

​

Sharing

Share your agent with teammates or external users. Open the “Integration & Sharing” dialog and use the Sharing tab to:

• Invite by Email — Send direct invitations and manage access roles
• Share Link — Generate a shareable URL that others can use to access the agent
• Password Protection — Optionally add a password to restrict access

​

MCP Integration

Integrate your agent directly into external applications using the MCP protocol. Switch to the Integration tab to get ready-to-use code snippets in Python or Node.js, along with buttons to copy your MCP URL and Access Token. See our integration guides for detailed examples on connecting agents to your applications.

​

Related Documentation