Build Enterprise AI Agent from Scratch: Series Overview

Tue, 07 Apr 2026 00:00:00 +0000

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 )
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 ChatOpenAI for ChatOllama (free, runs locally). You’ll need installed with a model like llama3.2.

Series Outline

Chapter	Title	Key Topics
Ch 0	Series Overview (this post)	Motivation, prerequisites, roadmap
	Introduction to AI Agents	Agent loop, chatbot vs. agent, when NOT to use agents
	Components & Context Engineering	4 core components, Prompt Eng vs. Context Eng, token budget
	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.example showing which variables are needed
Ollama switch instructions are in a callout box in every chapter with LLM code

Let’s build.