Authors:
- Jaysse Chen(jaysse@google.com) - Field Solution Architect
- Olivier Zhang(olivierzhang@google.com) - Field Solution Architect
Building Production-Ready Conversational AI Agents: A Practical Guide
Introduction
Conversational AI has evolved from simple chatbots to sophisticated agents that can handle complex, multi-step workflows with real-time interactions. But how do you build an AI agent that’s reliable, doesn’t hallucinate information, and can guide users through complex processes? This guide explores the architecture and patterns behind building production-ready live API agents.
What is a Live API Agent?
A live API agent is an AI-powered conversational system that:
- Interacts with users through natural language (voice or text)
- Calls external APIs and functions to retrieve real-time data
- Maintains conversation state across multiple interactions
- Enforces workflow logic to ensure task completion
- Operates in real-time with minimal latency
Unlike traditional chatbots that rely solely on pre-trained knowledge, live API agents actively query systems and databases to provide accurate, current information.
The Core Challenge: Preventing AI Hallucinations
One of the biggest challenges in building AI agents is hallucination - when the AI generates plausible but incorrect information. This happens when the model:
- Fills gaps in knowledge with made-up facts
- Recalls outdated information from training data
- Confuses similar but different concepts
- Generates responses without verifying current data
The Solution: Mandatory Function Calling
The key to preventing hallucinations is enforcing mandatory function calls before the agent provides information. Here’s how it works:
User: "Tell me about Product X"
❌ WRONG: Agent responds from memory
"Product X has features A, B, and C..."
✅ CORRECT: Agent calls function first
1. Call get_product_details(product_id="X")
2. Receive actual data from API
3. Respond with verified information
Architecture of a Live API Agent
1. System Components
A robust live API agent consists of several key components:
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ Frontend │ │ Backend │ │ External │
│ (User I/O) │◄───────►│ (Agent Logic) │◄───────►│ APIs │
│ │ │ │ │ │
│ - Audio I/O │ WSS │ - Workflow Track │ HTTP │ - Databases │
│ - Video Feed │ │ - State Mgmt │ │ - Cloud Services│
│ - UI Updates │ │ - Function Calls │ │ - AI Models │
└─────────────────┘ └──────────────────┘ └─────────────────┘
2. Workflow Tracker
The workflow tracker is the brain that ensures the agent follows a logical sequence:
Core Responsibilities:
- Track which functions have been called
- Determine the current workflow step
- Enforce mandatory function calls
- Prevent step-skipping
- Maintain conversation state
Example Workflow:
class WorkflowStep(Enum):
GREETING = "greeting"
INFORMATION_GATHERING = "information_gathering"
OPTION_PRESENTATION = "option_presentation"
SELECTION_CONFIRMATION = "selection_confirmation"
FINALIZATION = "finalization"
COMPLETION = "completion"
3. Function Call Enforcement
Each workflow step has required functions that MUST be called:
WORKFLOW_FUNCTIONS = {
WorkflowStep.INFORMATION_GATHERING: ["get_user_info"],
WorkflowStep.OPTION_PRESENTATION: ["fetch_available_options"],
WorkflowStep.SELECTION_CONFIRMATION: ["validate_selection"],
WorkflowStep.FINALIZATION: ["process_request"],
}
The system won’t advance to the next step until all required functions are executed.
Implementation Patterns
Pattern 1: Function Call Tracking
Automatically track every function call to maintain workflow state:
def track_function_call(func):
"""Decorator to track function calls"""
@wraps(func)
def wrapper(*args, **kwargs):
function_name = func.__name__
params = extract_params(args, kwargs)
# Execute function
result = func(*args, **kwargs)
# Record in workflow tracker
tracker.record_function_call(function_name, params, result)
return result
return wrapper
@track_function_call
def get_product_details(product_id: str) -> dict:
"""Fetch product information from API"""
response = api.get(f"/products/{product_id}")
return response.json()
Pattern 2: Dynamic Prompt Generation
Generate prompts that adapt based on workflow state:
def get_dynamic_prompt():
"""Generate context-aware prompts"""
tracker = get_workflow_tracker()
workflow_state = tracker.get_workflow_summary()
prompt = "MANDATORY WORKFLOW ENFORCEMENT\n"
prompt += "="*80 + "\n"
# Show completed steps
if workflow_state["functions_called"]:
prompt += "✅ Functions Already Called:\n"
prompt += f"- {', '.join(workflow_state['functions_called'])}\n"
# Show current position
prompt += f"\n📍 Current Step: {workflow_state['current_step']}\n"
# Enforce next action
if workflow_state["next_required_functions"]:
next_func = workflow_state["next_required_functions"][0]
prompt += f"\n🚨 REQUIRED ACTION: Call `{next_func}` now\n"
prompt += "DO NOT respond without calling this function first.\n"
return prompt
Pattern 3: State Management
Track user selections and conversation context:
@dataclass
class ConversationState:
"""Track conversation state"""
current_step: WorkflowStep
user_selections: Dict[str, Any] = field(default_factory=dict)
called_functions: List[str] = field(default_factory=list)
completed_steps: Set[WorkflowStep] = field(default_factory=set)
def update_from_function(self, function_name: str, result: dict):
"""Update state based on function execution"""
if function_name == "get_user_info":
self.user_selections["user_id"] = result.get("user_id")
self.current_step = WorkflowStep.OPTION_PRESENTATION
elif function_name == "validate_selection":
self.user_selections["selected_option"] = result.get("option")
self.completed_steps.add(WorkflowStep.SELECTION_CONFIRMATION)
Real-Time Communication
WebSocket Architecture
For live, bidirectional communication:
// Frontend WebSocket client
class LiveAgentClient {
constructor(serverUrl) {
this.ws = new WebSocket(serverUrl);
this.setupEventHandlers();
}
setupEventHandlers() {
this.ws.onmessage = (event) => {
const data = JSON.parse(event.data);
if (data.type === 'audio') {
this.playAudio(data.audio);
} else if (data.type === 'function_call') {
this.showFunctionCall(data.function, data.params);
} else if (data.type === 'state_update') {
this.updateUI(data.state);
}
};
}
sendMessage(message) {
this.ws.send(JSON.stringify({
type: 'user_message',
content: message,
timestamp: Date.now()
}));
}
}
Backend WebSocket Handler
async def handle_websocket(websocket, path):
"""Handle WebSocket connections"""
agent = create_agent()
conversation_state = ConversationState()
async for message in websocket:
data = json.loads(message)
# Get current workflow prompt
dynamic_prompt = get_dynamic_prompt(conversation_state)
# Process message with AI
response = await agent.process(
user_message=data['content'],
context=dynamic_prompt
)
# Send response back
await websocket.send(json.dumps({
'type': 'agent_response',
'content': response
}))
Best Practices
1. Prompt Engineering for Function Enforcement
Make function calling non-negotiable in your prompts:
🚨 CRITICAL RULES - NO EXCEPTIONS:
1. ALWAYS call the required function BEFORE responding
2. NEVER provide information from memory
3. NEVER skip functions in the workflow sequence
4. Each function call is MANDATORY - not optional
FUNCTION SEQUENCE (must follow in order):
1. gather_information() - When user starts
2. fetch_options() - Before showing choices
3. validate_selection() - Before confirming
4. process_transaction() - To complete
2. Error Handling and Recovery
Handle failures gracefully:
def safe_function_call(func_name: str, params: dict) -> dict:
"""Execute function with error handling"""
try:
result = execute_function(func_name, params)
return {"status": "success", "data": result}
except ValidationError as e:
return {
"status": "error",
"error_type": "validation",
"message": f"Invalid parameters: {str(e)}"
}
except APIError as e:
return {
"status": "error",
"error_type": "api",
"message": "Service temporarily unavailable"
}
3. Logging and Monitoring
Track everything for debugging and optimization:
def log_interaction(
session_id: str,
workflow_step: str,
function_called: str,
user_input: str,
agent_response: str
):
"""Log interaction for analysis"""
logger.info({
"session_id": session_id,
"timestamp": datetime.now().isoformat(),
"workflow_step": workflow_step,
"function_called": function_called,
"user_input_length": len(user_input),
"response_time_ms": calculate_response_time(),
"success": True
})
4. Context Window Management
Keep prompts concise while maintaining necessary context:
def build_efficient_prompt(state: ConversationState) -> str:
"""Build minimal but complete prompt"""
# Only include recent history
recent_functions = state.called_functions[-5:]
# Only include active selections
active_selections = {
k: v for k, v in state.user_selections.items()
if v is not None
}
prompt = f"""
Current Step: {state.current_step}
Recent Actions: {', '.join(recent_functions)}
Active Selections: {json.dumps(active_selections)}
Next Required Action: {state.get_next_required_function()}
"""
return prompt
Security Considerations
1. Input Validation
Always validate user input before function calls:
def validate_user_input(input_data: dict, schema: dict) -> bool:
"""Validate input against schema"""
required_fields = schema.get("required", [])
for field in required_fields:
if field not in input_data:
raise ValidationError(f"Missing required field: {field}")
# Type checking
for field, value in input_data.items():
expected_type = schema.get("properties", {}).get(field, {}).get("type")
if not isinstance(value, get_python_type(expected_type)):
raise ValidationError(f"Invalid type for {field}")
return True
2. API Authentication
Secure your API calls:
def get_authenticated_headers(target_url: str) -> dict:
"""Get authenticated headers for API calls"""
token = get_id_token(target_url)
return {
"Content-Type": "application/json",
"Authorization": f"Bearer {token}"
}
Deployment Considerations
Scalability
Use serverless architecture for automatic scaling:
# Cloud Run configuration
service: live-agent-backend
runtime: python3.11
instance_class: F2
automatic_scaling:
min_instances: 0
max_instances: 100
target_cpu_utilization: 0.65
resources:
cpu: 2
memory: 2Gi
Monitoring
Track key metrics:
METRICS_TO_TRACK = {
"function_call_success_rate": "% of successful function calls",
"workflow_completion_rate": "% of completed workflows",
"average_response_time": "ms from input to response",
"error_rate_by_step": "errors per workflow step",
"user_satisfaction": "based on explicit feedback"
}
Real-World Applications
Live API agents are perfect for:
- E-commerce Assistants - Guide users through product selection and purchase
- Customer Support - Handle inquiries with real-time data access
- Booking Systems - Manage reservations with live availability checks
- Healthcare Navigation - Connect patients with services and information
- Financial Advisors - Provide personalized recommendations with current data
Conclusion
Building a production-ready live API agent requires:
Mandatory function calling to prevent hallucinations- Workflow enforcement to ensure task completion
- State management to track conversation context
- Real-time communication for natural interactions
- Robust error handling for reliability
- Comprehensive logging for optimization
By following these patterns and best practices, you can build AI agents that are reliable, accurate, and provide genuine value to users.
Additional Resources
- Prompt Engineering: Learn to write effective system prompts
- Function Calling APIs: Understand LLM function calling capabilities
- WebSocket Protocols: Master real-time bidirectional communication
- State Machines: Design robust workflow systems
- Cloud Architecture: Deploy scalable serverless applications
Remember: The key to a great AI agent isn’t just the language model - it’s the architecture around it that ensures reliability, accuracy, and user satisfaction.
About This Guide
This guide distills lessons from building production conversational AI systems handling thousands of real-time interactions. The patterns presented here have been battle-tested in real-world applications across e-commerce, customer service, and booking systems.
Questions or feedback? The conversational AI space is evolving rapidly, and we’d love to hear about your experiences building live API agents.