Learn AI Agents in the right order.
Start with the fundamentals, understand the capabilities that make agents useful, then move into architecture and hands-on building.
Build the mental model first.
This sequence is intentionally concept-first. Frameworks, SDKs, MCP, A2A, and advanced architectures make much more sense after the core agent model is clear.
What Is an AI Agent?
Understand the core idea of an AI agent, autonomy, goals, environments, and why agents are different from ordinary LLM applications.
How AI Agents Work
Follow the agent loop from input and reasoning to action, observation, state updates, and repeated decision-making.
Anatomy of an AI Agent
See how models, instructions, tools, memory, state, planning, and evaluation fit together inside an agentic system.
Reasoning in AI Agents
Learn how agents analyze tasks, break down problems, choose actions, and use reasoning as part of an execution loop.
Tool Use in AI Agents
Understand function calling, APIs, browsers, code execution, databases, and how agents safely take actions outside the model.
Memory in AI Agents
Learn short-term memory, long-term memory, state, retrieval, context engineering, and what agents should remember.
Planning in AI Agents
Explore task decomposition, plan generation, replanning, execution strategies, and where planning helps or hurts.
Reflection in AI Agents
Understand how agents critique outputs, learn from execution results, retry intelligently, and improve future actions.
Single-Agent vs Multi-Agent Systems
Learn when one agent is enough, when multiple agents help, and the trade-offs of delegation, coordination, and supervision.
Build Your First AI Agent
Bring the concepts together by building a simple working agent that can reason, call tools, maintain state, and complete a task.
Choose where to go deeper.
Once you understand the core loop, capabilities, and architecture, move into the domain that matches what you are trying to do.
Need a definition while learning?
Use the AIRundown AI Agents Glossary as the reference layer for agent terminology, protocols, architectures, and key concepts.
Open the Glossary →