AI Agent Developer Roadmap — From 0 → 100 (Fullstack Edition) by Navidreza Abbaszadeh

This doc will look like something you’d actually publish publicly — clean structure, sectioned by weeks → days, and each day gives you what to learn, build, and apply. It’s designed for a 4-week sprint, so in 30 days, you’ll go from zero to building real, monetizable AI agents.

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🧭 AI Agent Developer Roadmap

Go from Fullstack Dev → AI Agent Engineer in 30 days. Learn, Build, and Ship agents that think, remember, and act.

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📘 Overview

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🗓️ Week 1 — Foundations of AI Agents

🎯 Goal:

Understand how agents think, reason, and interact with data + tools.

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Day 1 — What Are AI Agents?

• Read:

  • LangChain Docs → Agents Overview
  • OpenAI Assistants Overview

• Watch: “What Are AI Agents?” on YouTube (LangChain or Autogen channels)

• Build: None yet

• Output: Write a short summary — “How AI Agents Work” (Markdown)

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Day 2 — LLMs & Prompts

• Learn:

  • How GPT-style models generate responses (tokens, temperature, context length)
  • Prompt engineering basics: system vs user prompts

• Build:

  • A small Node.js script that queries OpenAI API and prints response

• Output:

  • prompt-basics.js

    — CLI that answers user input

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Day 3 — Context & Memory

• Learn:

  • Embeddings (text → vector)
  • Vector Databases (Pinecone, Supabase, Chroma)

• Build:

  • Script that stores text in a vector DB and finds “similar” queries

• Output:

  • memory-demo.js

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Day 4 — Tool Use & Function Calling

• Learn:

  • OpenAI function calling
  • How LLMs decide which function to call

• Build:

  • Simple weather agent → User asks “What’s the weather in Paris?” → Agent calls API

• Output:

  • weather-agent.js

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Day 5 — Multi-Step Reasoning

• Learn:

  • ReAct pattern (Reason + Act)
  • How agents plan steps (e.g., search → analyze → summarize)

• Build:

  • Simple research bot that searches Google + summarizes results

• Output:

  • research-agent.js

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Day 6–7 — Mini Project: Personal Research Assistant

• Combine everything:

  • Memory (vector DB)
  • Function calling (search)
  • Multi-step reasoning

• Build:

  • CLI or small web app: “Research anything, get a summary + sources”

• Output:

  • Project:
    research-assistant

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🧩 Week 2 — Building Smart Agents with LangChain / OpenAI

🎯 Goal:

Learn to use LangChain or OpenAI Assistants API to make real, persistent AI agents.

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Day 8 — Setup LangChain or Assistants API

• Install and configure:

  bash
  npm install langchain openai
  

• Read: LangChain JS docs: https://js.langchain.com

• Build: Hello-world agent using LangChain’s

  initializeAgentExecutor

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Day 9 — Adding Tools to Agents

• Add APIs (Google Search, News, etc.)

• Build: “News Summarizer Agent” — reads headlines and summarizes daily news

• Output:

  • news-agent.js

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Day 10 — Memory & Knowledge Integration

• Add long-term memory with embeddings

• Connect to Supabase or Pinecone

• Build: “Q&A Agent” that remembers previous topics

• Output:

  • memory-agent.js

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Day 11 — Multi-Agent Systems

• Learn:

  • How multiple agents collaborate (researcher, writer, editor)

• Build:

  • 2-agent system: “Researcher” + “Summarizer”

• Output:

  • multi-agent-demo.js

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Day 12–13 — Mini Project: Smart Notion Assistant

• Build an agent that:

  • Reads your Notion workspace (via API)
  • Answers questions about notes
  • Summarizes or updates pages

• Stack:

  • Next.js + LangChain + Supabase

• Output:

  • notion-assistant

    project

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Day 14 — Reflection + Publish

• Document your projects on GitHub
• Write a README with screenshots
• Post “Built my first AI agent using LangChain” on X/LinkedIn

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💻 Week 3 — Fullstack AI Agent Applications

🎯 Goal:

Integrate backend (Node.js + Prisma) and frontend (Next.js) into a complete agent app.

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Day 15–16 — Backend Setup

• Stack: Next.js API routes + LangChain backend
• Setup Prisma + Postgres for user data
• Build endpoint:
  /api/agent

  → handles agent logic

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Day 17–18 — Frontend Chat UI

• Build modern chat interface:

  • Use React Query / SWR
  • Stream responses from the backend

• Output:

  • Chat.tsx

    component
  • Live agent conversation

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Day 19 — Add User Authentication

• Use Clerk or NextAuth

• Save user sessions + conversation history

• Output:

  • Auth + Dashboard working

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Day 20–21 — Mini Project: FocusFlow AI Assistant

• Build productivity-focused agent:

  • Input: “Plan my day with my 5 tasks”
  • Output: AI-generated schedule, focus tips, summaries

• Output:

  • Deployed on Vercel
  • Shared online

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💰 Week 4 — Monetize, Automate & Scale

🎯 Goal:

Turn agents into real business leverage (MVP SaaS, API, or client work).

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Day 22–23 — Deploy & Scale

• Deploy on Vercel + Railway
• Add logging (PostHog / Sentry)
• Add simple billing (LemonSqueezy / Stripe)

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Day 24–25 — Marketing Basics

• Write clear landing page copy
• Add waitlist or pricing tiers
• Create short demo video (Loom or TikTok)

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Day 26–27 — Launch

• Post on Product Hunt or X
• Share build journey threads
• Ask for user feedback

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Day 28–30 — Optimize & Reflect

• Add caching, rate limits
• Refactor architecture for scalability
• Write blog post: “How I Built an AI Agent in 30 Days”

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🧠 Bonus Ideas for After 30 Days

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🏁 Summary

By the end of 30 days, you’ll: ✅ Understand LLMs, memory, and tools ✅ Build and deploy multiple agents ✅ Create a SaaS-level agent app ✅ Learn to monetize and scale your ideas