Cx Agent Studio

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name: cx-agent-studio description: Guide and instructions for using Google Customer Experience Agent Studio (CX Agent Studio). Use when creating conversational agents, writing or structuring instructions with XML tags, setting up few-shot examples, or building evaluation test cases (Golden or Scenario).

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CX Agent Studio

Customer Experience Agent Studio (CX Agent Studio) is a minimal code conversational agent builder built on the Agent Development Kit (ADK), representing the evolution of Dialogflow CX.

Core Capabilities

AI-Augmented Building: Generate agents using Gemini with a 1-2 sentence goal.

Bi-directional Streaming: Ultra-low latency voice interactions.

Asynchronous Tool Calling: Maintains natural conversation flow during backend calls.

Quick Actions

1. Generating an Agent with AI

To generate an agent automatically:

Provide a clear 1-2 sentence goal.

Optionally provide up to 5 knowledge documents (under 8MB total) like FAQs or tool catalogs.

*Note: Only works for the root agent and empty agents.*

2. Architecture & Design

Agents: Root (steering) agents orchestrate tasks and delegate to sub-agents. Read references/agents.md.

Flows: Integrate legacy Dialogflow CX flows for deterministic business logic (auth, sequential validation). Read references/flows.md.

Variables: Store and retrieve runtime conversation data. Read references/variables.md.

3. Writing Agent Instructions

Agent instructions guide the model's behavior, persona, and tool/agent usage.

Syntax References:

- Variables: {variable_name} - Tools: {@TOOL: tool_name} - Sub-Agents: {@AGENT: Agent Name}

For complex instructions or recommended XML formatting, read: references/instructions.md

Best Practices: Start simple, use specific/structured instructions, flat parameter structures. Read references/best-practices.md.

4. Tools & Callbacks

Tools: Connect your agent to external systems. Wrap complex APIs in Python tools to reduce context overhead. Read references/tools.md.

Callbacks: Advanced Python hooks (before_agent_callback, after_model_callback, etc.) to control execution, validate states, or inject custom JSON payloads. Read references/callbacks.md.

5. Guardrails & Safety

Guardrails: Protect against prompt attacks and enforce Responsible AI policies. Read references/guardrails.md.

6. Agent Evaluation

Evaluation ensures agent performance via automated test cases.

Scenario Test Cases: AI-generated simulated user conversations based on a user goal.

Golden Test Cases: Specific, ideal conversation paths for regression testing.

For detailed evaluation metrics, personas, and test case creation, read: references/evaluation.md