Understanding Generative AI, AI Agents, and Agentic AI — A Developer's Guide
Explore the three progressive levels of AI capability: Generative AI, AI Agents, and Agentic AI. Learn the differences, use cases, and frameworks for building LLM-powered systems.
Understanding the 3 Levels of AI in LLM-Powered Apps
In my journey to build LLM-powered systems, I realized there are three progressive levels of AI capability:
- Generative AI
- AI Agents
- Agentic AI
Each level builds on the previous one — moving from simple content generation to intelligent reasoning and autonomous decision-making.
1️- Generative AI (Gen AI)
Definition
Generative AI focuses on creating new content — text, images, audio, or videos — using large language models (LLMs) and diffusion models.
At this level, your application interacts directly with the LLM through prompts. The model doesn’t take any real-world actions; it just generates outputs based on input text.
Core Skills Needed
- Prompt Engineering: Crafting clear, structured, and goal-oriented prompts to guide model behavior.
- Context Engineering: Supplying relevant context (e.g., documents, user data) to improve response accuracy.
- Prompt Programming (DSPy): A more advanced approach to automate and structure prompt creation programmatically.
Example
- ChatGPT generating a blog post draft from a topic
- Midjourney generating an image from a text description
2️- AI Agents
Definition
An AI Agent is a system that combines Generative AI + Tools (Actions).
While Gen AI can only generate, an AI Agent can decide and act — it interprets a goal, plans a response, and executes actions via tools you define.
How It Works
- The LLM interprets the task and decides if it needs external actions
- The Tools (functions/APIs) perform these actions — such as searching the web, querying a database, sending an email, or writing a file
- The Agent Framework manages the interaction loop between the LLM and tools
Popular Frameworks
- LangChain
- LlamaIndex
- LangGraph
- CrewAI
- OpenAI Functions / ReAct pattern
Example
A research assistant app that:
- Takes a user question
- Uses a search tool to find relevant information
- Summarizes and presents the result — all autonomously
3️- Agentic AI
Definition
Agentic AI represents the next evolution — systems with autonomous reasoning, planning, and collaboration capabilities.
In this level, AI Agents can:
- Break complex tasks into multi-step plans
- Reflect on their actions and improve results
- Collaborate with other AI agents (multi-agent systems)
- Operate continuously with minimal human input
Key Traits
- Multi-step reasoning: The system plans before executing
- Goal decomposition: Large tasks are broken into smaller subtasks
- Autonomous decision-making: It chooses the best next step itself
Example
An “AI Project Manager” that receives a project brief, creates tasks, assigns them to specialized AI agents (e.g., one for research, one for code generation, one for documentation), monitors progress, and reports the results.
Techniques and Frameworks
- LangGraph (for structured agent orchestration)
- AutoGen / OpenDevin / CrewAI (multi-agent mode)
- Planning-based reasoning models (e.g., Tree-of-Thoughts, ReAct, Reflexion)
Summary Table
| Level | Core Capability | Involves | Example |
|---|---|---|---|
| Generative AI | Content generation | Prompts + LLM | ChatGPT writing an essay |
| AI Agent | Decision + Action | LLM + Tools | Agent that searches the web & summarizes |
| Agentic AI | Autonomous reasoning + planning | Multi-agent orchestration | AI system managing multiple AI collaborators |