Build Enterprise AI Agent from Scratch: Series Overview
There is no shortage of AI agent tutorials on the internet. Most of them show you how to build an agent in 50 lines of code — it calls a search tool, formats an answer, and you’re done. That’s fine for a weekend demo.
But production is different.
In production, your agent needs to handle adversarial inputs without leaking sensitive data. It needs to recover gracefully when a tool fails mid-task. It needs to remember context across sessions, not just within a single conversation window. You need to know exactly what your LLM did when a user reports a bug — and calculate how much it cost. And before you ship any of this, you need a principled way to measure whether it actually works.
I wrote this series because I spent years building enterprise AI agent systems and found that most resources stop right before the hard parts begin.
Who This Series Is For
This series is for software engineers and ML practitioners who:
- Have basic Python skills and understand what a Large Language Model (LLM) does
- Have heard of LangChain/LangGraph but haven’t gone deep into production usage
- Want to build agents that are deployable, observable, and maintainable — not just impressive in a notebook
You don’t need to have built an agent before. But you should be comfortable reading Python code and understand concepts like API calls and environment variables.
What You’ll Build
Across eight chapters, we build a fully-featured AI agent incrementally. Each chapter adds a new capability. By the end, you’ll have:
- A working ReAct agent powered by LangGraph with proper state management
- Multiple integrated tools with safe context injection (no secrets in the message stream)
- NeMo Guardrails for input/output content safety
- Human-in-the-loop interrupts for high-stakes decisions
- Four-layer memory architecture: in-context, episodic, semantic (RAG), and procedural
- Full Langfuse observability: traces, spans, cost tracking, and payload sanitization
- A 3-stage evaluation pipeline: rule-based checks, LLM-as-judge, and custom rubric scoring
Real-World Background
The patterns in this series are not theoretical. They come from working on two different enterprise AI systems:
- An automated document generation agent that uses a multi-step LangGraph workflow to produce structured outputs from unstructured inputs
- A multimodal chatbot that creates and edits presentation files through natural language, with sandboxed code execution for chart generation
Both systems run in production with real users. The lessons in this series — especially around memory management, observability, and evaluation — come directly from debugging and improving those systems over time.
All code in this series is original and generalized. No proprietary details, internal tooling, or credentials from those systems are used.
Prerequisites
Required:
- Python 3.10+
- An OpenAI API key (sign up at platform.openai.com)
- Basic familiarity with how LLMs / ChatGPT work conceptually
Helpful but not required:
- Experience with FastAPI or any Python web framework
- Basic understanding of async Python (
async def,await)
💡 Using a local model instead? Every chapter that contains OpenAI code includes a note showing how to swap
ChatOpenAIforChatOllama(free, runs locally). You’ll need Ollama installed with a model likellama3.2.
Series Outline
| Chapter | Title | Key Topics |
|---|---|---|
| Ch 0 | Series Overview (this post) | Motivation, prerequisites, roadmap |
| Ch 1 | Introduction to AI Agents | Agent loop, chatbot vs. agent, when NOT to use agents |
| Ch 2 | Components & Context Engineering | 4 core components, Prompt Eng vs. Context Eng, token budget |
| Ch 3 | LangChain & LangGraph Intro | Messages, @tool, StateGraph, Hello World agent |
| Ch 4 | Build Your First Agent | AgentState, tool_node, streaming, SQLite checkpointer |
| Ch 5 | Tools, Guardrails & Safety | Tool design, AgentContext, NeMo Guardrails, HITL |
| Ch 6 | Memory Management | 4-layer memory, MongoDB checkpointer, FAISS RAG |
| Ch 7 | Tracing with Langfuse | trace_agent_execution, @trace_tool, cost callbacks |
| Ch 8 | Evaluation System | 3-stage pipeline, DeepEval GEval, custom rubrics |
How to Follow Along
Each chapter is self-contained. You can read sequentially or jump to a specific topic. However, Chapter 4 is the foundation — if you’re skipping ahead to Ch 5–8, make sure you’ve at least read Ch 4 first.
Code conventions used throughout:
- File path is shown as a comment at the top of every snippet:
# agent/graph.py - Secrets always use environment variables:
os.environ.get("OPENAI_API_KEY") - Every chapter includes a
.env.exampleshowing which variables are needed - Ollama switch instructions are in a callout box in every chapter with LLM code
Let’s build.
