Core Concepts & Foundations
The vocabulary and mental models behind agentic AI — what makes software an agent, and how it differs from the tools you already use.
What Is Agentic AI? A Plain-English Introduction
A plain-English introduction to agentic AI: what it is, how it differs from ordinary AI tools, and why software that acts on goals matters now.
Agentic AI vs Traditional AI: What's the Difference?
Agentic AI vs traditional AI compared clearly: how they handle goals, autonomy, tools, and consequences, plus when each approach fits a task.
How Agentic AI Works: The Core Loop Explained
How agentic AI works, explained through its core loop: perceive, plan, act, observe, and repeat, with the role of tools, memory, and stopping.
The Building Blocks of an AI Agent
The building blocks of an AI agent explained: the reasoning model, tools, memory, planning, and guardrails that turn a model into an actor.
What Makes AI "Agentic"? Key Characteristics Defined
What makes AI agentic? Five defining characteristics: goal-direction, autonomy, tool use, adaptation, and persistence, explained with clear examples.
Autonomy in AI: How Much Independence Do Agents Really Have?
How much autonomy do AI agents really have? Explore the spectrum of independence, common control levels, and how to set safe boundaries for agents.
Agentic AI Explained for Non-Technical Readers
Agentic AI explained for non-technical readers using everyday analogies. Understand what AI agents do, why it matters, and what to watch for.
The History and Evolution of AI Agents
The history and evolution of AI agents, from early symbolic systems and robotics to modern language-model agents that plan and use tools.
Reactive vs Deliberative Agents: A Simple Breakdown
Reactive vs deliberative agents explained simply: how each makes decisions, their trade-offs in speed and foresight, and why hybrids often win.
What Is an AI Agent? Definition, Examples, and Types
What is an AI agent? A clear definition with real examples and the main types, from simple reactive agents to goal-driven, learning, and multi-agent systems.
Single-Agent vs Multi-Agent Systems Explained
Single-agent vs multi-agent systems explained: when one agent suffices, when several collaborating agents help, and the trade-offs of each design.
The Difference Between Chatbots and AI Agents
The difference between chatbots and AI agents, explained clearly: conversation versus action, single replies versus goals, and where each one fits.
Goal-Oriented AI: How Agents Pursue Objectives
Goal-oriented AI explained: how agents turn objectives into action, break goals into subtasks, track progress, and handle conflicting or unclear goals.
What Is Agentic Reasoning?
What is agentic reasoning? How AI agents think through goals step by step, decide actions, reflect on results, and adapt, with the limits to know.
Perception, Reasoning, and Action: The Agent Triad
Perception, reasoning, and action form the agent triad. Learn how these three capabilities combine to let AI agents sense, decide, and act on goals.
Understanding Agent Environments
Understanding agent environments: what an environment is, key properties like observability and predictability, and why they shape how agents are built.
What Are Large Action Models (LAMs)?
What are large action models (LAMs)? How they extend language models from generating text to taking actions, how they differ from LLMs, and their uses.
From LLMs to Agents: How Language Models Became Actors
From LLMs to agents: how language models gained tools, memory, and loops to become actors that pursue goals, and what changed to make the leap possible.
The Role of Memory in Agentic AI
The role of memory in agentic AI: how short-term and long-term memory keep agents coherent, recall past work, and the challenges of managing it well.
What Is Tool Use in AI Agents?
What is tool use in AI agents? How agents call external capabilities to gather information and act, why it matters, and how tools define an agent's limits.
Agentic AI vs Generative AI: Key Distinctions
Agentic AI vs generative AI explained: learn how each works, where they overlap, and the key distinctions that determine which fits your project best.
What Is an Agentic Workflow?
An agentic workflow lets AI plan, act, and adapt across multiple steps. Learn how agentic workflows work, their building blocks, and when to use them.
Deterministic vs Probabilistic Agent Behavior
Deterministic vs probabilistic agent behavior shapes reliability and flexibility in AI. Learn the trade-offs and how to combine both in agentic systems.
The Concept of Agency in Artificial Intelligence
What does agency in artificial intelligence really mean? Explore the concept of agency, its core ingredients, and how it separates agents from tools.
What Are Autonomous Agents?
Autonomous agents are AI systems that pursue goals and take actions with minimal human input. Learn how they work, where they help, and their limits.
Understanding the Agent–Environment Interface
The agent-environment interface defines how an AI senses and acts on its world. Learn how observations, actions, and feedback shape agent behavior.
What Is Human-in-the-Loop AI?
Human-in-the-loop AI keeps people involved in key decisions an AI makes. Learn how it works, why it matters for safety, and how to design it well.
Levels of AI Agent Autonomy Explained
AI agent autonomy ranges from simple assistants to fully independent systems. Explore the levels of autonomy and how to choose the right one for a task.
What Is Agentic Orchestration?
Agentic orchestration coordinates multiple AI agents and tools toward a shared goal. Learn how it works, common patterns, and why it matters for reliability.
The Difference Between Automation and Agentic AI
Automation follows fixed rules; agentic AI reasons and adapts. Learn the difference between automation and agentic AI and when each approach fits best.
What Is a Cognitive Architecture in AI?
A cognitive architecture defines how an AI agent perceives, reasons, remembers, and acts. Learn what it includes and why it shapes intelligent behavior.
Symbolic vs Neural Approaches to AI Agents
Symbolic vs neural approaches to AI agents offer different strengths in logic and learning. Compare the two and see why hybrid designs often win.
What Is Embodied Agentic AI?
Embodied agentic AI gives agents a physical or simulated body to sense and act in the world. Learn how embodiment shapes perception, action, and learning.
Understanding Agent Goals, Rewards, and Utility
Agent goals, rewards, and utility define what an AI agent tries to achieve. Learn how these concepts guide behavior and why specifying them well is hard.
What Is a Digital Worker?
A digital worker is an AI agent that handles end-to-end tasks like a software colleague. Learn what digital workers do, how they differ from bots, and their limits.
Key Terminology in Agentic AI: A Glossary
A clear agentic AI glossary defining key terminology, from agents and tools to orchestration and human-in-the-loop, so you can follow the field with confidence.
What Is Agentic RAG (Retrieval-Augmented Generation)?
Agentic RAG combines retrieval-augmented generation with an agent that reasons about what to retrieve. Learn how agentic RAG works and why it beats basic RAG.
The Difference Between Copilots and Autonomous Agents
Copilots assist a human in real time; autonomous agents act on their own. Learn the difference between copilots and autonomous agents and when each fits.
What Is a World Model in Agentic AI?
A world model lets an AI agent predict how its environment behaves and plan ahead. Learn what world models are, why they matter, and how agents use them.
Common Myths and Misconceptions About AI Agents
Separate fact from fiction with this guide to common myths and misconceptions about AI agents, covering autonomy, reliability, intelligence, and real limits.