The Landscape of Agentic AI

System	One-Line Definition
Rule-Based Bot	“Follows predefined rules.”
LLM Chatbot	“Answers using pretrained knowledge.”
RAG	“Answers using retrieved external knowledge.”
Tool-Using AI	“Can use external functions/tools.”
Agentic AI	“Can pursue goals autonomously.”
Multi-Agent System	“Multiple AI agents collaborate together.”

Level	System Type	Core Idea	Main Capability	Knowledge Source	Tool Usage	Planning / Reasoning	Memory	Autonomy Level	Typical Workflow	Example
1	Rule-Based Chatbot	Hardcoded program logic	Deterministic responses	Static rules / database	❌ None	❌ None	❌ None	Very Low	if-else → response	FAQ bot
2	Trained AI Chatbot	Pretrained language intelligence	General conversational ability	Model training data	❌ Usually none	✅ Language reasoning	Limited context window	Low	user query → LLM answer	Basic ChatGPT-style assistant
3	RAG System	LLM + retrieval augmentation	Grounded answers using external knowledge	Retrieved chunks + model knowledge	Optional	✅ Contextual reasoning	Usually short-term	Medium	retrieve → inject context → generate answer	Enterprise document assistant
4	Tool-Using AI	LLM + external functions/APIs	Can interact with external systems	Retrieved data + tool outputs	✅ Yes	✅ Yes	Usually limited	Medium	reason → call tool → answer	AI with calculator/search/API access
5	Agentic AI	LLM + tools + planning + action loops	Goal-oriented autonomous execution	Retrieved data + tools + memory + observations	✅ Central capability	✅ Multi-step planning	✅ Often persistent	High	observe → think → act → evaluate → repeat	Autonomous troubleshooting agent
6	Multi-Agent Systems	Multiple collaborating agents	Distributed specialized intelligence	Shared memory + tools + retrieval	✅ Extensive	✅ Collaborative planning	✅ Shared memory	Very High	agents coordinate and delegate tasks	Autonomous DevOps/Research systems