Basic information
| Field | Description |
|---|---|
| Name | The agent’s display name. Shown on the dashboard card, in the test drawer, and in any UI that lists agents. |
| Description | A short summary of what the agent does. Shown on the dashboard and used as the agent’s description when it’s used in Agent-to-Agent configurations. |
| Tags | Optional labels for organizing agents (e.g., support, finance, internal, v2). |
Custom Clauses
Custom Clauses are the six building blocks of an agent’s system prompt. When Dev Mode is off, Studio assembles these into a complete system prompt automatically.| Clause | What it defines | Example |
|---|---|---|
| Identity | Who the agent is | ”You are Maya, a customer support assistant for Acme Corp” |
| Personality | Tone, style, and communication approach | ”You are warm, concise, and professional. Avoid jargon.” |
| Purpose | The agent’s primary goal | ”Your goal is to help customers resolve billing questions and account issues” |
| Knowledge | What the agent knows and doesn’t know | ”You have expertise in our product catalog and pricing. You do not have access to real-time inventory.” |
| Rules | Behavioral constraints and guardrails | ”Never share other customers’ data. Always escalate legal questions to the legal team.” |
| Response Format | How answers should be structured | ”Respond in plain text. Use bullet points for multi-step instructions. Keep answers under 200 words.” |
Evaluation strategy
Controls how the agent reasons and whether it can use tools. See Agents → Evaluation Strategies for the full breakdown.This field is set automatically when you add MCP servers or other agents to your configuration. Studio switches to
react to enable the tool-use loop. You can override this manually in Dev Mode.Structured output
An optional JSON schema that forces the agent to return data in a specific format instead of free-form text. Use this when:- The agent feeds into another system or pipeline that expects structured data
- You want to extract specific fields from the agent’s analysis
- You’re building a classification or extraction agent
{ "intent": "...", "confidence": 0.0, "suggested_action": "..." }.
Next steps
Inference
Select the model that powers this agent.