Getting Started with LLM Agents in Production

Building LLM agents that actually work in production requires more than just connecting to an API. In this guide, I'll share patterns and practices I've learned from deploying agentic systems at scale.

What Are LLM Agents?

An LLM agent is an AI system that can:

• Reason through complex problems step by step
• Plan and break down tasks into manageable pieces
• Execute actions using tools (search, APIs, code execution)
• Remember context across multiple interactions

Unlike simple chatbots, agents can take autonomous actions to accomplish goals.

Core Architecture Components

1. The Agent Loop

The fundamental pattern is a loop:

Observation → Thought → Action → Result → Observation...

Your agent observes the environment, thinks about what to do, takes an action, observes the result, and repeats until the goal is achieved.

2. Tool Selection

Give your agent specific, well-documented tools:

• Web Search - DuckDuckGo, Google Search API
• Calculator - For mathematical operations
• Code Execution - Python sandbox for computations
• Document Retrieval - RAG for domain-specific knowledge
• APIs - Custom business logic endpoints

3. Memory Management

Agents need memory to handle multi-turn conversations:

• Short-term: Conversation history within a session
• Long-term: Persistent user preferences and facts
• Working memory: Scratchpad for complex reasoning

Production Considerations

Error Handling

Agents will fail. Plan for it:

• Timeout limits - Prevent infinite loops
• Retry logic - Handle transient failures gracefully
• Fallback responses - When all else fails, have a backup
• Human escalation - Know when to hand off to a human

Observability

You need visibility into what your agent is doing:

• Step-by-step logging - Trace every decision
• Cost tracking - LLM calls add up quickly
• Latency metrics - User experience matters
• Success/failure rates - Measure outcomes

Rate Limiting

Protect your infrastructure:

• Per-user limits - Prevent abuse
• Global rate limiting - Protect downstream services
• Queue management - Handle traffic spikes

Example: DeepAgent Implementation

Here's a simplified version of the DeepAgent I built:

from langgraph.graph import StateGraph
from langchain_ollama import ChatOllama

# Define agent state
class AgentState:
    messages: list
    next_step: str

# Create the graph
workflow = StateGraph(AgentState)

# Add nodes for each skill
workflow.add_node("think", think_skill)
workflow.add_node("plan", plan_skill)
workflow.add_node("search", search_skill)
workflow.add_node("report", report_skill)

# Define transitions
workflow.add_conditional_edges("think", route_next_step)
workflow.add_edge("plan", "execute")
workflow.add_edge("search", "synthesize")
workflow.add_edge("report", END)

The key insight: treat your agent as a state machine, not just a prompt pipeline.

Deployment Strategy

Local Models vs. Cloud APIs

Factor	Local (Ollama)	Cloud (OpenAI)
Cost	Hardware only	Per-token pricing
Latency	Fast (no network)	Network dependent
Control	Full	Limited
Quality	Varies by model	Generally higher

For production, I recommend a hybrid approach:

• Use cloud APIs for complex reasoning tasks
• Use local models for simpler, high-volume operations

Infrastructure Stack

My recommended production stack:

• FastAPI - High-performance async API
• Redis - Rate limiting and session storage
• PostgreSQL - Persistent memory storage
• Celery - Background task processing
• LangGraph - Agent orchestration

Key Takeaways

1. Start simple - Don't over-engineer your first agent
2. Measure everything - You can't improve what you don't track
3. Plan for failures - Agents will make mistakes
4. Design for observability - Know what your agent is doing
5. Iterate quickly - Real-world feedback beats theory

Next Steps

Ready to build your own agent? Check out these resources:

• LangGraph documentation
• DeepAgent project on GitHub
• FastAPI best practices

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Have questions about building production LLM agents? Let's connect.