What Is an AI Agent? A 2026 Plain-English Guide
What separates an AI agent from a chatbot, and why everyone's talking about them in 2026. Plain-English explainer with examples.
Tag
fundamentals
11 articles tagged fundamentals. Browse the full blog.
Tool Calling in LLMs Explained: How AI Agents Use External Functions
A clear breakdown of tool calling in LLMs: what it is, how JSON schema works, how structured outputs work, and why function calling is what separates a.
RAG vs Agents: When to Use Each in 2026
RAG fetches knowledge. Agents take action. This guide explains the real difference, when each approach fits, and how agentic RAG combines them for harder.
Prompt Engineering for AI Agents: What's Different in 2026
Agent prompting is not chatbot prompting. Learn how to design system prompts, tool descriptions, planning prompts, and reflection prompts that make agents.
How Do AI Agents Actually Work? Inside the Loop
A technical-but-accessible breakdown of how AI agents reason, plan, use tools, and remember things. From the basic loop to ReAct and real-world examples.
Chain of Thought vs ReAct: Which AI Reasoning Pattern Wins?
A deep-dive comparison of Chain of Thought and ReAct prompting patterns - what each one does, where each one breaks down, and how to choose the right one.
Building Your First AI Agent in 2026: A Practical Guide
A practical walkthrough for building your first AI agent from scratch: choosing a goal, picking a framework, scaffolding the loop, adding tools and.
AI Agents vs LLMs: What's the Real Difference in 2026?
LLMs generate text. Agents act on the world. This guide explains the real architectural difference, when each is the right tool, and how they work together.
AI Agent Pricing in 2026: Subscription, Usage, and Hidden Costs
A practical breakdown of how AI agent pricing actually works in 2026: subscription vs usage-based models, token costs, enterprise tiers, BYOK economics,.
AI Agent Architecture Patterns: ReAct, Plan-Execute, and More
A practical guide to the most important AI agent architecture patterns: ReAct, Plan-Execute, Reflection, Multi-Agent, and more - with real examples and.
AI Agent Memory Systems Explained: Short-Term, Long-Term, Vector Stores
A deep dive into how AI agents remember things: short-term context windows, long-term storage, episodic vs semantic memory, vector databases, and.