cx-agent-studio

# 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`