Building custom AI agents requires understanding core architectures, implementing proper patterns, and following best practices. This guide takes you from basic concepts to production-ready implementations with step-by-step workflows.
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Start with a simple agent that can process inputs, maintain conversation context, and generate responses. Follow these steps to build your first agent:
Set up the base agent class with LLM client and configuration
Implement conversation history and context management
Handle user input and generate appropriate responses
Test your agent and deploy to production
# Basic Agent Implementation
class SimpleAgent:
def __init__(self, llm_client, system_prompt=None):
self.llm = llm_client
self.system_prompt = system_prompt or "You are a helpful AI assistant."
self.conversation_history = []
self.max_history = 10
async def process(self, user_input):
"""Process user input and generate response"""
# Add user input to history
self.conversation_history.append({
"role": "user",
"content": user_input
})
# Prepare messages for LLM
messages = [
{"role": "system", "content": self.system_prompt}
] + self.conversation_history[-self.max_history:]
# Get LLM response
response = await self.llm.chat.completions.create(
model="gpt-4",
messages=messages,
temperature=0.7
)
# Extract and store response
assistant_message = response.choices[0].message.content
self.conversation_history.append({
"role": "assistant",
"content": assistant_message
})
return assistant_message
# Usage Example
agent = SimpleAgent(llm_client)
response = await agent.process("What is machine learning?")
print(response)
The ReAct pattern combines reasoning with action-taking, making agent behavior more transparent and controllable. Here's how to implement it:
Create structured prompts for reasoning steps
Extract actions and parameters from LLM responses
Run selected tools and gather observations
Continue reasoning until final answer is reached
# ReAct Agent Implementation
class ReActAgent:
def __init__(self, llm_client, tools):
self.llm = llm_client
self.tools = tools
self.max_iterations = 5
async def execute(self, task):
"""Execute task using ReAct pattern"""
prompt = f"""Task: {task}
You will solve this step by step:
Thought: [Your reasoning]
Action: [Tool to use]
Action Input: [Input for the tool]
Observation: [Tool output - I'll provide this]
Available tools: {', '.join(self.tools.keys())}
When you have the answer:
Thought: [Final reasoning]
Final Answer: [Your answer]
Begin:
Thought: """
for iteration in range(self.max_iterations):
# Get LLM response
response = await self.llm.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}]
)
content = response.choices[0].message.content
# Check for final answer
if "Final Answer:" in content:
return self.extract_final_answer(content)
# Extract and execute action
if "Action:" in content:
action = self.extract_action(content)
action_input = self.extract_action_input(content)
if action in self.tools:
observation = await self.tools[action].run(action_input)
prompt += content + f"\nObservation: {observation}\nThought: "
return "Could not complete task within iteration limit"
# Usage
tools = {"search": SearchTool(), "calculate": CalculatorTool()}
agent = ReActAgent(llm_client, tools)
result = await agent.execute("Find the current weather in Tokyo")
Extend your agent's capabilities by integrating various tools and services. Follow this workflow to add tool support:
Create base tool class with standard methods
Build specific tools for different capabilities
Add tools to registry for agent access
Process tool calls with error handling
| Tool Type | Use Case | Setup Time | Complexity |
|---|---|---|---|
| Web Search | Current information retrieval | 5 minutes | Low |
| Database Query | Structured data access | 15 minutes | Medium |
| API Integration | External service connection | 30 minutes | Medium |
| File Processing | Document analysis | 20 minutes | High |
Get started quickly with our pre-built agent templates:
Deploy your agent to production with proper monitoring, scaling, and reliability features:
Implement comprehensive error recovery and logging
Track metrics, performance, and usage patterns
Set up auto-scaling and load balancing
Launch to production and monitor performance
# Production Agent Service with FastAPI
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import logging
from datetime import datetime
app = FastAPI(title="Production Agent API")
class AgentRequest(BaseModel):
message: str
session_id: str = None
context: dict = {}
class AgentResponse(BaseModel):
response: str
session_id: str
timestamp: datetime
metrics: dict
class ProductionAgent:
def __init__(self):
self.sessions = {}
self.rate_limiter = RateLimiter(max_requests=100)
self.metrics = {
"total_requests": 0,
"successful_requests": 0,
"avg_response_time": 0
}
async def process(self, request: AgentRequest) -> AgentResponse:
"""Process request with monitoring"""
start_time = datetime.now()
try:
# Check rate limits
if not await self.rate_limiter.check(request.session_id):
raise HTTPException(status_code=429, detail="Rate limit exceeded")
# Process message
response = await self.generate_response(request.message)
# Update metrics
self.update_metrics(True, start_time)
return AgentResponse(
response=response,
session_id=request.session_id,
timestamp=datetime.now(),
metrics={"processing_time": (datetime.now() - start_time).total_seconds()}
)
except Exception as e:
logging.error(f"Error: {str(e)}")
self.update_metrics(False, start_time)
raise
agent = ProductionAgent()
@app.post("/chat")
async def chat(request: AgentRequest) -> AgentResponse:
"""Chat endpoint with production features"""
return await agent.process(request)
@app.get("/health")
async def health():
"""Health check endpoint"""
return {"status": "healthy", "metrics": agent.metrics}
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Test and validate agent performance with comprehensive evaluation metrics and benchmarks.