- 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).
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. Readreferences/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