The Evolution of AI Agents

Level 1: Small Context Window LLMs

The early transformer chatbots worked simply: Text In → LLM → Text Out. Their Achilles’ heel was a tiny memory—just a few thousand tokens. Conversations were short, brittle, and often forgetful. When ChatGPT first launched, it could only handle 4k tokens.

Level 2: Large Context Window LLMs

Next came the expansion. Models like Claude and ChatGPT grew their context windows into the hundreds of thousands. Suddenly, they could read entire documents, remember longer conversations, and provide answers with richer continuity.

Level 3: LLM + Tools (The RAG Era)

The breakthrough came when LLMs learned to use tools. This approach, known as Retrieval-Augmented Generation (RAG), opened the door to fresh knowledge—search APIs, databases, calculators—supercharging outputs with accuracy and timeliness.

Level 4: Multimodal + Tools + Memory

The evolution deepened when agents started processing multiple data types. Text, images, and even audio became valid inputs. With memory added, interactions gained persistence, allowing conversations that “remembered” context across sessions.

Level 5: Reasoning Agents with Memory

This is the era we’re living in.

Modern agents don’t just respond—they decide. They combine short-term, long-term, and episodic memory with ReAct-style reasoning loops. They call APIs, search the web, trigger workflows, and stitch together multimodal outputs.