AI Agent Knowledge Library

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Core knowledge for understanding how AI agents work, including reasoning, memory, tools, planning, autonomy, and multi-agent systems.

9 Published resources

Explore: Foundations Memory & Context Multi-Agent Systems Reasoning & Planning Tools & Actions

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One capable AI agent compared with a coordinated group of specialized AI agents.

Comparison Beginner

Single-Agent vs Multi-Agent Systems

Learn how single-agent and multi-agent systems differ, what extra coordination costs, and how to choose the simplest architecture that works.

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Latest resources

9 published articles

An AI agent compares an action result with success criteria, identifies a gap, and sends a correction back into the agent loop.

Explainer Beginner

Reflection in AI Agents: How Agents Review, Correct, and Improve Their Work

Learn how AI agents use feedback, critique, and execution review to detect mistakes, revise their approach, and improve results without endless retry loops.

An AI agent converts a goal into connected tasks, uses tools, and adjusts its route after feedback.

Explainer Beginner

Planning in AI Agents: From Goals to Adaptive Action

Learn how AI agents turn goals into ordered tasks, account for dependencies and constraints, use tools, track progress, and replan when reality changes.

AI agent memory lifecycle showing observe, decide what matters, store, retrieve when relevant, use, and update or forget.

Explainer Beginner

Memory in AI Agents: How Agents Remember, Retrieve, and Forget

Learn how AI agent memory works, from context windows and working memory to persistent stores, retrieval, updating, forgetting, and memory quality.

AI agent decision layer connected to search, databases, code execution, and external applications.

Explainer Beginner

Tool Use in AI Agents: How Agents Act Beyond the Model

Learn how AI agents select tools, prepare arguments, execute functions and APIs, observe results, recover from errors, and stay within safe permission boundaries.

Goal, context, constraints, and options flow into an AI agent reasoning core, which produces a decision and next action.

Explainer Beginner

Reasoning in AI Agents: How Agents Decide What to Do Next

Learn how AI agents interpret goals, break down tasks, handle uncertainty, choose tools, reflect on results, and decide what to do next.

Nine labeled AI agent components arranged around a central agent core: model, instructions, tools, memory, state, planning, observation, guardrails, and execution loop.

Explainer Beginner

Anatomy of an AI Agent: The 9 Core Components

A beginner-friendly breakdown of the model, instructions, tools, memory, state, planning, feedback, guardrails, and execution loop inside an AI agent.

Circular AI agent execution loop connecting Goal, Reason, Decide, Act, Observe, Update State, and Repeat around a goal-directed agent.

Explainer Beginner

How AI Agents Work: The Complete Execution Loop

Follow the seven-stage execution loop that lets an AI agent reason, choose actions, use tools, learn from results, and keep working toward a…

AI agent loop showing goal, reasoning, action, observation, and adaptation around a central intelligent system.

Explainer Beginner

What Is an AI Agent? A Practical Mental Model

A practical explanation of what makes an AI agent different from a chatbot or fixed workflow, and how the agent loop turns model…