You know what AI agents are—now let's build one properly. This guide covers the complete architecture of production-ready AI agents, from core components to deployment patterns, with real implementation examples.
What You'll Learn
- Core agent architecture patterns (ReAct, Plan-and-Execute, Reflexion)
- Tool integration and function calling
- Memory systems (short-term, long-term, semantic)
- Multi-agent orchestration
- Production deployment and monitoring
- Real code examples with LangChain and LangGraph
Agent Architecture Fundamentals
An AI agent consists of four core components that work together in a reasoning loop.
Core Agent Components
LLM Brain
The reasoning engine that makes decisions, plans actions, and generates responses.
Examples:
GPT-4, Claude 3.5, Gemini Pro
Tools
Functions the agent can call to interact with external systems and perform actions.
Examples:
API calls, database queries, web search, file operations
Memory
Storage for conversation history, learned information, and context across interactions.
Types:
Short-term (conversation), Long-term (vector DB), Semantic (knowledge graph)
Planning/Reasoning
The logic that determines what to do next based on goals, observations, and available tools.
Patterns:
ReAct, Plan-and-Execute, Reflexion, Tree of Thoughts
The Agent Loop
Every agent follows this basic loop:
- Observe: Receive input (user query, environment state)
- Think: Reason about what to do (LLM generates plan)
- Act: Execute tool/action
- Observe: Get result from action
- Repeat: Continue until goal is achieved
Agent Architecture Patterns
Different patterns suit different use cases. Here are the most common production patterns.
1. ReAct (Reasoning + Acting)
The most popular pattern. Agent reasons about what to do, takes action, observes result, and repeats.
How it works:
- Agent receives task: "Book a flight to NYC"
- Thought: "I need to search for flights first"
- Action: Call search_flights(destination="NYC")
- Observation: Returns 3 flight options
- Thought: "I should ask user preference"
- Action: Ask user to choose
- Continue until task complete
# ReAct Agent Implementation
from langchain.agents import create_react_agent
from langchain.tools import Tool
from langchain_openai import ChatOpenAI
# Define tools
tools = [
Tool(
name="search_flights",
func=search_flights_api,
description="Search for flights to a destination"
),
Tool(
name="book_flight",
func=book_flight_api,
description="Book a specific flight"
)
]
# Create agent
llm = ChatOpenAI(model="gpt-4")
agent = create_react_agent(llm, tools)
# Run
result = agent.invoke("Book cheapest flight to NYC tomorrow")Best for:
- • Customer support (check order, update info, send email)
- • Research tasks (search, analyze, summarize)
- • Data analysis (query DB, generate charts, explain)
2. Plan-and-Execute
Agent creates a complete plan upfront, then executes each step. Better for complex, multi-step tasks.
How it works:
- Agent receives task: "Analyze competitor pricing and create report"
- Planning Phase: Create detailed plan
- Step 1: Scrape competitor websites
- Step 2: Extract pricing data
- Step 3: Analyze trends
- Step 4: Generate visualizations
- Step 5: Write report
- Execution Phase: Execute each step sequentially
- Reflection: Review results, adjust plan if needed
Best for:
- • Complex research projects
- • Multi-step data pipelines
- • Report generation
- • Tasks requiring upfront planning
3. Reflexion (Self-Reflection)
Agent evaluates its own performance and learns from mistakes. Includes a reflection step after each action.
How it works:
- Agent attempts task
- Evaluates result quality
- If unsatisfactory, reflects on what went wrong
- Adjusts approach and retries
- Stores learnings in memory
Best for:
- • Code generation (test, debug, improve)
- • Content creation (draft, review, refine)
- • Tasks requiring quality iteration
Tool Integration & Function Calling
Tools are what make agents powerful. Here's how to implement them properly.
1. Define Tool Schema
Tools need clear descriptions so the LLM knows when and how to use them.
# Tool Definition Example
from langchain.tools import tool
from pydantic import BaseModel, Field
class SearchInput(BaseModel):
query: str = Field(description="Search query")
max_results: int = Field(default=5, description="Max results")
@tool("web_search", args_schema=SearchInput)
def web_search(query: str, max_results: int = 5) -> str:
"""Search the web for information.
Use this when you need current information or facts.
Returns: JSON string with search results"""
# Implementation
results = search_api(query, limit=max_results)
return json.dumps(results)Key Points:
- • Clear, descriptive function name
- • Detailed docstring explaining when to use it
- • Type hints for all parameters
- • Return structured data (JSON preferred)
2. Common Tool Patterns
API Tools
Call external APIs
@tool
def get_weather(city: str):
response = requests.get(
f"api.weather.com/{city}"
)
return response.json()Database Tools
Query databases
@tool
def query_orders(user_id: str):
query = "SELECT * FROM orders"
results = db.execute(query)
return results.to_json()RAG Tools
Search knowledge base
@tool
def search_docs(query: str):
results = vectorstore.similarity_search(
query, k=3
)
return format_results(results)Action Tools
Perform actions
@tool
def send_email(to: str, subject: str):
email_client.send(
to=to,
subject=subject
)
return "Email sent"3. Error Handling & Validation
Production tools need robust error handling.
# Production-Ready Tool
@tool
def update_customer_info(
customer_id: str,
field: str,
value: str
) -> str:
"""Update customer information in CRM"""
try:
# Validate inputs
if field not in ["email", "phone", "address"]:
return f"Invalid field: {field}"
# Check permissions
if not has_permission(customer_id):
return "Permission denied"
# Update
crm.update(customer_id, {field: value})
return f"Updated {field} successfully"
except Exception as e:
logger.error(f"Tool error: {e}")
return f"Error: {str(e)}"Memory Systems
Memory allows agents to maintain context, learn from interactions, and personalize responses.
Short-Term Memory
Conversation history within current session
# Conversation Buffer Memory
from langchain.memory import ConversationBufferMemory
memory = ConversationBufferMemory(
memory_key="chat_history",
return_messages=True
)Stores last N messages in memory
Long-Term Memory
Persistent storage across sessions using vector DB
# Vector Store Memory
from langchain.memory import VectorStoreRetrieverMemory
memory = VectorStoreRetrieverMemory(
retriever=vectorstore.as_retriever()
)Retrieves relevant past interactions
Semantic Memory
Structured knowledge and facts learned over time
# Entity Memory
from langchain.memory import ConversationEntityMemory
memory = ConversationEntityMemory(
llm=llm
)Tracks entities and relationships
Production Memory Architecture
Hybrid Approach (Recommended):
- Short-term: Redis for current session (fast access)
- Long-term: Vector DB for semantic search (Pinecone, Weaviate)
- Structured: PostgreSQL for entities and facts
User query → Check short-term (Redis) → Search long-term (Vector DB) → Combine context → Send to LLM
Multi-Agent Orchestration
Complex tasks often require multiple specialized agents working together.
Multi-Agent Patterns
1. Hierarchical (Supervisor Pattern)
One supervisor agent delegates tasks to specialized worker agents.
Supervisor Agent → Research Agent, Writing Agent, Review Agent
2. Sequential (Pipeline)
Agents process tasks in sequence, each adding value.
Data Agent → Analysis Agent → Visualization Agent → Report Agent
3. Collaborative (Peer-to-Peer)
Agents communicate and collaborate as equals.
Code Agent ↔ Test Agent ↔ Debug Agent (iterate together)
# Multi-Agent System with LangGraph
from langgraph.graph import StateGraph, END
from typing import TypedDict
class AgentState(TypedDict):
task: str
research_results: str
draft: str
final_output: str
# Define agents
def research_agent(state):
results = research_tool(state["task"])
return {"research_results": results}
def writing_agent(state):
draft = llm.invoke(f"Write based on: {state['research_results']}")
return {"draft": draft}
def review_agent(state):
final = llm.invoke(f"Review and improve: {state['draft']}")
return {"final_output": final}
# Build graph
workflow = StateGraph(AgentState)
workflow.add_node("research", research_agent)
workflow.add_node("write", writing_agent)
workflow.add_node("review", review_agent)
workflow.add_edge("research", "write")
workflow.add_edge("write", "review")
workflow.add_edge("review", END)
app = workflow.compile()When to Use Multi-Agent Systems
- Task complexity: When single agent becomes too complex
- Specialization: Different tasks need different expertise
- Parallel processing: Multiple tasks can run simultaneously
- Quality control: Separate agents for creation and review
Production Deployment
Moving from prototype to production requires additional considerations.
Infrastructure
- →API Gateway: Rate limiting, authentication
- →Queue System: Redis/RabbitMQ for async tasks
- →Caching: Redis for frequent queries
- →Load Balancer: Distribute traffic
Monitoring
- →Logging: Track all agent actions
- →Metrics: Response time, success rate, cost
- →Tracing: LangSmith, Weights & Biases
- →Alerts: Error rates, latency spikes
# Production Agent with Monitoring
from langsmith import traceable
import logging
import time
@traceable(run_type="agent")
def run_agent(user_input: str):
start_time = time.time()
try:
# Log input
logging.info(f"Agent input: {user_input}")
# Run agent
result = agent.invoke(user_input)
# Track metrics
duration = time.time() - start_time
metrics.record("agent_duration", duration)
metrics.record("agent_success", 1)
return result
except Exception as e:
logging.error(f"Agent error: {e}")
metrics.record("agent_error", 1)
raiseCost Optimization
- Caching: Cache common queries and tool results
- Model selection: Use GPT-3.5 for simple tasks, GPT-4 for complex
- Prompt optimization: Shorter prompts = lower cost
- Streaming: Stream responses for better UX without extra cost
- Rate limiting: Prevent abuse and runaway costs
Typical Production Costs:
1000 agent interactions/day ≈ $50-200/month (depending on complexity)
Security Best Practices
- Input validation: Sanitize all user inputs
- Tool permissions: Limit what tools can access
- API key rotation: Regularly rotate credentials
- Audit logging: Track all agent actions
- Sandboxing: Run code execution in isolated environments
- PII handling: Mask sensitive data in logs
Agent Frameworks Comparison
| Framework | Best For | Learning Curve | Production Ready |
|---|---|---|---|
LangChain Most popular | General-purpose agents Great ecosystem, many integrations | Medium | ✓ Yes |
LangGraph By LangChain team | Complex multi-agent systems Graph-based workflows | High | ✓ Yes |
AutoGPT Autonomous | Fully autonomous tasks Experimental, research-focused | Low | ⚠ Experimental |
CrewAI Role-based | Multi-agent collaboration Simple API, role-based design | Low | ✓ Yes |
Semantic Kernel Microsoft | Enterprise .NET/Python apps Great Azure integration | Medium | ✓ Yes |
Custom (DIY) Build your own | Specific requirements Full control, no dependencies | High | Depends |
Recommendation
- Starting out: LangChain (best documentation, community)
- Complex workflows: LangGraph (powerful but steeper learning curve)
- Simple multi-agent: CrewAI (easiest to get started)
- Enterprise .NET: Semantic Kernel (Microsoft ecosystem)
- Full control: Build custom (when you have specific needs)
Real-World Agent Examples
Customer Support Agent
Tools:
- Check order status (API)
- Update shipping address (DB)
- Process refund (Payment API)
- Search knowledge base (RAG)
- Send email (Email API)
Memory:
Conversation history + customer profile
Pattern:
ReAct (responds to queries, takes actions)
Research Assistant
Tools:
- Web search (Google API)
- Academic search (arXiv, PubMed)
- Summarization (LLM)
- Citation extraction
- Report generation
Memory:
Research findings + source tracking
Pattern:
Plan-and-Execute (creates research plan, executes)
Code Review Agent
Tools:
- Read code files
- Run linters
- Execute tests
- Check security vulnerabilities
- Generate suggestions
Memory:
Codebase context + style guidelines
Pattern:
Reflexion (review, suggest, iterate)
Sales Outreach Agent
Tools:
- CRM integration
- Company research (web scraping)
- Email personalization
- Schedule meetings (Calendar API)
- Follow-up tracking
Memory:
Lead history + interaction tracking
Pattern:
Multi-agent (research → personalize → send → follow-up)
Production Best Practices
✓ Do
- →Start simple, add complexity gradually
- →Test tools independently before integration
- →Log all agent decisions and actions
- →Implement fallbacks for tool failures
- →Set max iterations to prevent infinite loops
- →Use structured outputs (JSON) from tools
- →Monitor costs and set budgets
- →Version your prompts and track changes
✗ Don't
- →Give agents unrestricted access to systems
- →Skip input validation and sanitization
- →Deploy without rate limiting
- →Ignore error handling in tools
- →Use production data in development
- →Hardcode API keys in code
- →Assume agents will always work correctly
- →Forget to test edge cases and failures
Key Takeaways
- →Four core components: LLM brain, tools, memory, and planning/reasoning logic
- →Choose the right pattern: ReAct for general tasks, Plan-and-Execute for complex workflows, Reflexion for quality iteration
- →Tools are critical: Well-designed tools with clear descriptions and error handling make or break agents
- →Memory matters: Use hybrid approach (Redis + Vector DB + SQL) for production
- →Multi-agent for complexity: Break complex tasks into specialized agents
- →Production requires: Monitoring, logging, error handling, security, and cost optimization
- →Start with LangChain: Best ecosystem and documentation for beginners
Ready to Build Your Agent?
You now have the architecture knowledge. Time to implement.