Topic
AI Agents Fundamentals
12 posts on ai agents fundamentals — part of glossary on the n4n AI blog.
What is an AI agent? A plain-English explanation
A practical definition of AI agents for engineers — what they are, how they work, and where the hype diverges from reality.
What is agentic AI, and why does it matter now
A precise technical definition of agentic AI, how it differs from chatbots and RAG, concrete architecture patterns, and common misconceptions engineers should know.
How AI agents use tools to complete tasks
A technical explainer of how AI agents use tools — function calling, execution loops, and the patterns that let models act on the world.
How AI agents plan, act, and use tools
A technical breakdown of AI agent architecture — planning loops, tool calling patterns, and execution models engineers can actually use.
Coding agents explained: how Claude Code and Devin work
A practitioner's breakdown of coding agents — what they are, how Claude Code and Devin operate under the hood, and what engineers get wrong about them.
Building your first AI agent: a beginner's guide
A practical, code-first walkthrough for engineers building their first AI agent — covering architecture, tool calling, memory, and common failure modes.
Autonomous agents vs copilots: what's the difference
A practitioner's comparison of autonomous agents and copilots across capabilities, cost, latency, ergonomics, and ecosystem — with a clear verdict by use case.
AutoGPT explained: what it does and how it works
A technical breakdown of AutoGPT — what it is, how its autonomous loop works, and what engineers get wrong about it.
AI agent memory: short-term vs long-term explained
Understand how AI agent memory works — short-term context windows versus long-term persistent stores — with concrete patterns, code examples, and common pitfalls.
AI agent frameworks compared: LangGraph vs CrewAI
A practitioner's head-to-head comparison of LangGraph and CrewAI across architecture, state management, control flow, and deployment — with a clear verdict for your use case.
AI agents vs LLMs: what's the difference
A practitioner's comparison of AI agents and LLMs across capabilities, cost, latency, ergonomics, and ecosystem — with a clear verdict on when to use each.
AI agents vs chatbots: what's the difference
A practitioner's comparison of AI agents vs chatbots across architecture, cost, latency, ergonomics, and failure modes — with a clear verdict by use case.
More topics in glossary
- Structured Outputs & JSON Mode19
- Hallucination in LLMs11
- Sampling Parameters: Top-p, Top-k & Penalties11
- Context Window & Context Length10
- Fine-Tuning Fundamentals9
- Foundation Models: Base vs Instruct vs Chat9
- Model Families & Naming Conventions: GPT-5, Claude, Gemini 3, Llama 4, Mistral, DeepSeek, Qwen, Grok9
- Grounding & Fact-Checking in AI8
- LLM Benchmarks: MMLU, HumanEval, SWE-bench & GPQA8
- Max Tokens, Stop Sequences & Output Truncation8
- Quantization Formats: GGUF, GPTQ, AWQ & INT4/INT88
- Chain-of-Thought Prompting7