Agentic AI
Agents, tool use, memory, planning, and multi-agent systems — the flagship pillar.
36 articles
Advanced Reasoning Frameworks: ToT, GoT, and Reflexion
Tree of Thoughts, Graph of Thoughts, Reflexion, and least-to-most prompting compared, plus when the extra cost is actually worth paying.
What Are Agent Skills? Roles and Capabilities Explained
What are agent skills? Named, triggerable behaviors with clear steps and gotchas. See how skills, roles, and capabilities differ before Module 20.
Agentic Prompting: How AI Agents Act on Your Prompts
Agentic prompting means writing system and tool prompts an AI agent can act on, not just reply to, using ReAct and plan-and-execute patterns.
How AI Agents Work: The Think-Act-Observe Loop
How do AI agents work? Four parts in a loop, environment, tools, a brain, and think-act-observe, dissected using the Claude Code you already run.
Automatic Prompt Optimization: Stop Hand-Tuning Prompts
Automatic prompt optimization uses eval scores, not guesswork, to improve prompts. Learn how APE and DSPy find better wording than tuning by hand.
AI Agent Autonomy Levels: The Full Spectrum, Explained
AI agent autonomy runs on a spectrum, not a switch, from scripted automation to fully autonomous. Here's how to pick the right level for the stakes.
Chain of Thought for AI Agents: Reasoning Picks the Tool
Chain of thought prompting works differently in an agent: it picks the next tool, not just the final answer. See where it helps, where it wastes tokens.
Chatbot vs AI Agent: What's Actually the Difference?
A chatbot answers your question and stops. An AI agent takes your goal and acts until it's done. Here's the clearest beginner explanation of both.
How to Use Claude Code: Your First Autonomous Loop
How to use Claude Code as an agentic terminal: watch a real autonomous loop, understand permission prompts, and write your first CLAUDE.md.
Why So Many Builders Run Claude Code Inside Cursor
Developers install Claude Code inside Cursor because it splits one job in half: Cursor edits live, Claude Code works autonomously in the background.
Claude Code vs Cursor: A Beginner's Decision Guide
Claude Code and Cursor aren't rivals, they're peer environments. Install Claude Code, run your first terminal session, and learn when to use each.
CodeAct vs Tool Calling: When Agents Should Write Code
CodeAct lets agents write and run code instead of JSON tool calls, cutting round-trips but adding sandbox risk. Here's how to choose.
Best AI Coding Agents in 2026: A Full Comparison
See how the best AI coding agents of 2026, Cursor, Claude Code, Codex, and more, actually compare, and why the harness now decides which one wins.
Cursor @ Context: How to Feed the Model the Right Code
Learn how Cursor's @-mentions and codebase indexing work, and why any answer's quality is bounded by the context you deliberately choose to give it.
Context Engineering, Part 2: Production Memory Systems
AI agents lose track of earlier context long before their window is full. Here's how compaction, tool clearing, and memory tools solve it in production.
Cursor Agent Mode vs Ask Mode: A Risk-Dial Guide
Cursor agent mode vs ask mode: they're risk dials, not buttons. Learn which to reach for, plus Tab and Cmd-K for fast, low-risk edits.
Decision Journaling: Why Your Agent Needs a Decisions.md
A decision log records what you chose, why, and what you rejected, so neither you nor your agent re-argues a settled call. Here's how to build one.
How to Design Tools for AI Agents (Without Guessing)
How to design tools for AI agents: clear names, tight schemas, and descriptions that stop your agent from picking the wrong tool. A hands-on guide.
How Does Tool Calling Work: The tool_use Loop Explained
How does tool calling work? The model stops mid-turn, emits a tool_use block naming a tool and its input, and your code runs it and returns a tool_result.
How to Install Cursor AI Editor: Setup and First Tour
Learn how to install Cursor AI editor step by step, import your VS Code settings, and use the model picker with real confidence.
Multi-Agent Prompting and Orchestration, Explained Clearly
Multi-agent orchestration coordinates specialized AI agents through supervisor and worker roles, hand-offs, and debate to solve tasks one agent can't.
Plan-and-Execute: Why AI Agents Plan Before Acting
Plan-and-execute separates an agent's planning from its execution, catching costly mistakes before any code runs. Here's how it maps to Plan mode.
What Is an Agentic Workflow? Prompting vs the Loop
What is an agentic workflow? It's a loop: the model plans, acts, reads the actual result, and decides its next step, instead of answering once.
ReAct Agent Pattern Explained: Build the Loop Yourself
ReAct interleaves reasoning and tool calls through the tool_use stop reason. Build the raw loop yourself and see exactly what LangChain automates.
Reasoning Models & Test-Time Compute: When to Spend
Test-time compute lets reasoning models think longer before answering, but longer isn't always smarter; it pays off on hard tasks, not easy ones.
Agent Reflection Pattern: Why Grounded Beats Intrinsic
Learn the agent reflection pattern (Self-Refine, Reflexion) and why grounded self-correction works while intrinsic self-checking often fails.
Safety-First Defaults: How to Safely Use AI Coding Agents
Learn how to safely use AI coding agents with branch-only workflows, permission modes, and a trust-then-verify diff habit.
Spec-Driven Development: Specs That Steer Your AI Agent
Spec-driven development turns a vague feature request into a versioned spec an AI coding agent builds from, catching ambiguity before code does.
The Augmented LLM: The Building Block of AI Agents
An augmented LLM is a plain LLM plus retrieval, tools, and memory, the primitive Anthropic says every AI agent is built from.
The Agent Self-Improvement Loop: Evaluate, Then Fix
The agent self-improvement loop is plan, execute, evaluate against one number, then refine. Learn why skipping the metric turns improve into just change.
What Changed in Agentic AI in 2026: A Builder's Guide
Agentic AI in 2026: protocols like MCP and A2A stabilized, coding agents went autonomous, and memory became its own field. Here's what changed.
What Is an AI Agent, Really? A Workflow-vs-Agent Test
A workflow runs code you wrote in advance, an agent decides its next move after seeing a result. Here's Anthropic's exact test, applied to real examples.
How to Build AI Agents: What This Course Assumes First
This how to build ai agents course teaches you to build real agents inside Cursor and Claude Code, not a notebook, from zero to four shipped capstones.
When Not to Use AI Agents (And What to Build Instead)
When not to use AI agents comes down to one question: is there a real decision to make mid-task? Here's the full test, with five real scenarios.
Why Your AI Agent Won't Stop Looping (and the Fix)
Your AI agent won't stop looping because of one of three bugs: thrashing, no stop condition, or context bloat. Build, break, and fix a real loop here.
Your First AI Agent: A Real Delegated Task, Felt Not Coded
Delegate a real multi-step task to Claude Code and learn to read what your first AI agent actually did, why it worked, and where it corrected itself.