Understanding AI: Agentic vs Generative AI

Most explanations of AI are written for either researchers or complete beginners. If you're a builder - someone who wants to understand AI well enough to build with it - you're stuck between academic papers and "AI for dummies" content.

This guide fills that gap. I'll explain the concepts that actually matter for building AI-powered products, skip the hype, and connect everything to practical application.

Generative AI vs Agentic AI

This is the most important distinction in AI today, and most people conflate the two.

Generative AI: The Content Creator

Generative AI produces new content - text, images, code, audio, video. When you ask ChatGPT to write an email or DALL-E to create an image, that's generative AI.

How it works: Large language models (LLMs) like Claude and GPT-4 are trained on massive datasets of human-created content. They learn patterns in language and can generate new text that follows those patterns. They predict the most likely next token based on everything that came before.

Key characteristics:

Single-turn or multi-turn conversations
Input → Output (prompt → response)
No persistent memory between sessions
Can't take actions in the real world
Quality depends on training data and prompt quality

Examples: ChatGPT conversations, image generation, code completion, content drafting, language translation.

Agentic AI: The Autonomous Worker

Agentic AI goes beyond generating content. It takes actions, makes decisions, uses tools, and works toward goals autonomously. It's the difference between an AI that writes code and an AI that writes code, tests it, debugs failures, and deploys it.

How it works: An AI agent is an LLM connected to tools and given the ability to decide which tools to use and when. The agent receives a goal, breaks it into steps, executes each step using available tools, evaluates results, and adjusts its approach.

Key characteristics:

Goal-oriented (not just prompt-response)
Can use tools: file systems, APIs, databases, browsers
Makes sequential decisions
Can loop and self-correct
Operates autonomously within boundaries

Examples: Claude Code building features across multiple files, Devin writing and testing code, AI research agents that search the web and synthesize findings.

The Critical Difference

Generative AI answers questions. Agentic AI solves problems.

Ask generative AI "How do I add authentication to my Next.js app?" and it gives you a response. Ask an agentic AI the same question and it reads your codebase, chooses an auth library, implements it across your project, writes tests, and verifies everything works.

The shift from generative to agentic is the most important trend in AI right now. It's what makes vibe coding possible and what enables one-person AI businesses.

How LLMs Actually Work (Without the Math)

You don't need a PhD to build with AI, but understanding the basics helps you work with it more effectively.

Tokens and Context Windows

LLMs don't read words. They read tokens - chunks of text that might be a word, part of a word, or punctuation. "Understanding" is one token. "AI" is one token. The context window is how many tokens the model can process at once.

Why this matters for builders: Context window size determines how much of your codebase an AI agent can understand at once. Bigger windows mean better understanding. Claude's context window can handle 200K tokens - roughly 150,000 words or a very large codebase.

Temperature and Creativity

Temperature controls how "creative" or "deterministic" the model's output is. Low temperature (0) means the model always picks the most likely next token. High temperature (1+) means it introduces randomness.

Why this matters: For code generation, you usually want low temperature (predictable, correct output). For brainstorming, higher temperature gives you more diverse ideas.

System Prompts and Instructions

System prompts are instructions that shape how the model behaves. They're like giving an employee an onboarding manual before they start working.

Why this matters: Your CLAUDE.md file is a system prompt. The better your instructions, the better the output. This is why prompt engineering is a real skill.

AI Agents: How They Work

AI agents are the building blocks of agentic AI. Understanding their architecture helps you build better AI-powered applications.

The Agent Loop

Every AI agent follows the same basic loop:

Observe - Read the current state (your prompt, codebase, tool outputs)
Think - Decide what to do next (which tool to use, what to generate)
Act - Execute the decision (write a file, run a command, call an API)
Evaluate - Check the result (did the test pass? did the build succeed?)
Repeat - Go back to step 1 with new information

This loop is what makes agents powerful. They don't just generate output once - they iterate until the goal is met.

Tool Use

Tools are what give agents superpowers. A bare LLM can only generate text. An LLM with tools can:

Read and write files
Execute shell commands
Search the web
Query databases
Call external APIs
Take screenshots
Run tests

Claude Code has dozens of tools - file reading, writing, bash execution, web search, and more. The model decides which tool to use based on the current task.

Memory and Context

Agents need to remember what they've done. There are several approaches:

Conversation history - The simplest form. Everything said in the current session is in context. Limited by context window size.

Persistent memory - Files like CLAUDE.md that persist between sessions. The agent reads them at startup.

External knowledge - Databases, vector stores, or knowledge graphs that agents can query for information beyond their training data.

Multi-Agent Systems

The frontier of agentic AI is multi-agent systems - multiple agents working together on different parts of a problem.

Claude Code's subagent system is an example. The main agent can spawn specialized sub-agents to explore code, research approaches, or handle specific tasks in parallel. Each sub-agent has its own context and tools.

This pattern scales naturally: one agent for frontend, one for backend, one for testing. They coordinate through shared files and context.

Building With AI: A Practical Framework

If you want to build AI-powered applications, here's the framework I use.

Level 1: AI-Assisted Development

Use AI tools to help you write code faster. This is where most developers start.

Tools: GitHub Copilot, Cursor, ChatGPT Approach: Write code with AI autocomplete and chat assistance Best for: Individual productivity improvement

Level 2: AI-Powered Features

Add AI capabilities to your applications - chatbots, content generation, image analysis.

Tools: OpenAI API, Anthropic API, open-source models Approach: Call AI APIs from your application code Best for: Adding intelligence to existing products

Level 3: Agentic Development

Build with AI agents that write, test, and deploy code autonomously.

Tools: Claude Code, custom agents with AI SDKs Approach: Describe goals, let agents implement Best for: Rapid product development, vibe coding

Level 4: AI-Native Products

Build products where AI is the core experience, not a feature.

Tools: Agent frameworks, Claude Code SDK, custom tooling Approach: Design products around AI capabilities Best for: New product categories enabled by AI

Key Concepts for Builders

Prompt Engineering

The art of writing instructions that get consistent, high-quality results from AI. This isn't just about clever wording - it's about:

Providing clear context
Specifying output format
Including examples
Setting constraints
Breaking complex tasks into steps

RAG (Retrieval-Augmented Generation)

A pattern where you give AI access to external knowledge by retrieving relevant documents before generating a response. This is how chatbots answer questions about your specific data.

Fine-Tuning vs Prompting

Fine-tuning trains a model on your specific data. Prompting gives instructions at runtime. For most builders, good prompting is sufficient and much cheaper. Fine-tune only when prompting can't achieve the consistency you need.

Hallucinations

AI models sometimes generate confident-sounding but incorrect information. This is not a bug - it's a fundamental characteristic of how LLMs work. They predict likely text, and sometimes likely text is wrong.

Mitigation strategies:

Provide source material (RAG)
Ask for citations
Use structured output formats
Verify outputs with code (tests, type checking)
Human review for critical decisions

The Current AI Landscape (2026)

Model Providers

Anthropic (Claude) - Best for complex reasoning, coding, and agent-based workflows. Claude Opus for deep thinking, Sonnet for balanced speed/quality, Haiku for fast simple tasks.

OpenAI (GPT) - Broadest ecosystem, strong general capabilities. GPT-4 and successors for complex tasks, o1 for reasoning-heavy problems.

Google (Gemini) - Strong multimodal capabilities, long context windows. Deep integration with Google ecosystem.

Open Source - Llama, Mistral, and others. Best for cost-sensitive applications, on-premise deployment, and customization.

The Trend Lines

Models are getting cheaper and more capable every quarter
Context windows are growing (100K → 200K → 1M+)
Agent capabilities are the primary area of competition
Multimodal (text + image + audio + video) is becoming standard
Specialized models are emerging for specific industries

Getting Started

If you're new to building with AI:

Use AI tools daily - Start with Claude Code or Cursor for coding
Build something small - A chatbot, a content generator, an automation
Read the APIs - Understand how to call AI models programmatically
Think in agents - Design systems where AI acts, not just generates
Stay current - The field moves fast; follow developments weekly

For hands-on tutorials, check out:

How to Code AI - Getting started with AI programming
How to Code AI Agents - Building your first agent
AI Coding with Python - Python-specific AI development

Understanding AI: From Basics to Building (2026)