The Rise of Agentic AI
From simple chatbots to autonomous systems that reason, plan, and act. This interactive guide explores the evolution of AI agents, the powerful models that drive them, and the tools being built for this new frontier of software development.
What is an AI Agent?
An AI Agent is an autonomous system designed to perceive its environment, make decisions, and take actions to achieve specific goals. This section breaks down the core components that form the "mind" and "body" of an agent. Interact with the cards and the planner below to learn about each fundamental piece.
Perception
How the agent takes in information. This can be text from a user, data from an API, visual information from an image, or readings from sensors. It's the agent's "senses."
Planning
The "brain" of the agent. It breaks down a large goal into smaller, manageable steps. Powered by LLMs, this allows agents to devise sophisticated strategies to solve complex problems.
Action
How the agent interacts with its environment. This involves executing the planned steps by using "tools" like calling an API, running code, or sending an email. It's the agent's "hands."
Memory
Essential for learning and context. Short-term memory tracks the current task, while long-term memory stores past experiences, allowing the agent to improve over time.
✨ Plan with an Agent
Want to see how an agent thinks? Enter a high-level goal below and let Gemini break it down into an agentic plan, showing the Perception, Planning, and Actions required for each step.
The Journey to Autonomy
The path to modern AI agents wasn't instantaneous. It was a series of conceptual breakthroughs, building one on top of the other. This section traces the key steps, from giving models a 'thought process' to letting them interact with the real world.
Chain-of-Thought (CoT)
The discovery that instructing an LLM to "think step-by-step" dramatically improves its reasoning on complex tasks. This allows the model to simulate a logical process, forming the foundation of agent planning.
ReAct Framework
A crucial framework that combined reasoning with acting. ReAct created the iterative Thought -> Action -> Observation loop, giving the agent's "brain" a set of "hands" to interact with tools and the real world.
Autonomous Agents
Projects like Auto-GPT and BabyAGI captured the world's attention by putting the concepts into a fully autonomous loop. Given a high-level goal, they could self-generate tasks, execute them, and iterate until the objective was met.
LLM Release Timeline
Agent capabilities are directly tied to the power of the underlying Large Language Models. The timeline below highlights the key releases that have fueled the agentic AI revolution. Filter by company to see how the competitive landscape has evolved.
Model Releases Per Year
This chart visualizes the number of major model releases each year, showing the accelerating pace of innovation in the AI space. The data reflects the filtered timeline.
The Agentic Developer's Toolkit
As models grow more powerful, so do the tools for building with them. This is a survey of the new class of AI-native IDEs and platforms that are changing how developers write software, shifting from writing every line to directing intelligent agents.