Understand and build the loops, harnesses, and protocols that make AI agents reliable and autonomous — from your first agent loop to production-grade multi-agent systems.
An AI agent is a system that can take autonomous actions — calling tools, reading files, running code, making web requests — to complete a task over multiple steps without continuous human guidance. A chatbot responds to single messages. An agent operates in a loop: observe, think, act, observe again, until the task is complete.
No. This pathway covers the principles of agent architecture — loops, harnesses, tool access, memory — that apply regardless of whether you use LangChain, CrewAI, a custom harness, or Claude Code's built-in agent capabilities. Framework knowledge becomes much easier to acquire once you understand the underlying patterns.
13 articles, approximately 6 hours. This is an intermediate pathway — completing AI Foundations and Prompt Engineering first will make it significantly more approachable.
Understand what AI actually is — tokens, transformers, agents, and the landscape. Start here if you're new.
11 articles · ~4h →Go from vague requests to precise, reproducible AI outputs. The skill that underpins everything.
13 articles · ~5h →Go from zero to productive with Claude Code — the terminal AI coding agent that ships real projects.
15 articles · ~7h →Context vs Prompt vs Loop vs Harness Engineering
Four layers of the agent stack — how they nest, what breaks when you skip one, and which lever to fix when agents fail.
Types of AI Agents
Taxonomy by autonomy, loop architecture, domain, and tool access — with a decision matrix for choosing the right design.
What Is Loop Engineering?
The new paradigm beyond prompt engineering — and why it matters.
What Is an Agent Harness?
The scaffolding layer that makes AI agents reliable in production.
How to Build Your First Agent Loop
Step-by-step guide to building a functional AI agent loop.
Agentic Loop: stop_reason, tool_use, and end_turn
How stop_reason drives control flow and why checking for natural language text is an anti-pattern.
Cloudflare x402 Monetization Gateway for MCP and APIs
HTTP 402 stablecoin micropayments for agents — waitlist, flow, and builder checklist.
What Is MCP? Model Context Protocol Explained
The open protocol for connecting AI agents to any tool or service.
GPT-Realtime-2.1-mini: Reasoning Voice Agents at Mini Price
July 2026 API launch — reasoning + tools in the Realtime mini tier, session types, and cost math.
RAG vs MCP: Complete Guide to Context-Aware AI
When to use retrieval augmentation vs structured tool access.
What Are Embeddings? Vector Search Explained
The math behind semantic search and how agents use it.
Agent Markdown Files: SKILL.md, AGENT.md, CLAUDE.md
The files that give agents persistent identity and memory.
What Are Agent Skills?
Procedural memory for AI coding agents — complete guide.
Top 10 AI Agent Loops for Coding Workflows
Production-tested patterns for common agentic coding scenarios.
Agent Harness Engineering: When Scaffolding Wins
How LangChain jumped TerminalBench without changing the model.
The Agentic Era: How AI Agents Will Transform Everything
The big picture view from 2026 to 2030.
Practical AI adoption for your specific function — marketing, engineering, HR, finance, and more.
10 articles · ~4h →
