// section 1
Fundamentals: the basics, no jargon.
The starting point. What an AI agent actually is, what an LLM is, the difference between the major models, and what every business owner should know before deploying AI. Read these in order if you are new.
1.1 · What is an AI agent? Plain English for business owners.
The cornerstone explainer. What an agent is, what it is not, the difference between agents, chatbots, and Zapier, the parts of an agent (sub-agents, skills, tools), the 5-level autonomy ladder, and when to deploy one vs. hire a person.
1.2 · What is an LLM? The brain inside every AI agent.
The model behind the agent. What "large language model" means, how it predicts text, why it sometimes makes things up, the difference between training and inference, the major LLM families (Claude, ChatGPT, Gemini, Grok), and what every business owner actually needs to know.
1.3 · Claude vs ChatGPT vs Gemini vs Grok: which one wins for which job.
The honest comparison. Strengths and weaknesses of each frontier model, where each one quietly leads, where each one quietly fails, and a simple decision table you can use to pick the right tool. With actual examples, not marketing slides.
1.4 · Chatbot vs AI agent vs automation: how to tell them apart.
Three things people constantly confuse. What a chatbot does, what an agent does, what Zapier (and Make, n8n) does, and how to know which one solves your specific problem. With a decision tree you can use.
1.5 · Types of AI: language, image, vision, audio, and the rest.
AI is not one thing. Language models write and reason. Image models like Midjourney and Nano Banana generate visuals. Vision models read what is in a photo. Audio models transcribe and synthesize speech. What each one does, and which kinds your business probably wants.
1.6 · AI safety for business owners: hallucinations, guardrails, when NOT to use AI.
The honest map of risk. What hallucination actually means, why a confident-but-wrong answer costs you money, where to put hard guardrails, and the five tasks you should never let AI handle on its own. Skip the doom narrative. Read this and ship safely.
// section 2
Working with AI: the practical guides.
Use-case-driven, operator-level. How to deploy AI alongside your team without replacing anyone, which agents pay back fastest, how to think about autonomy, and the playbook for the most common business jobs (support, finance, content, ops).
2.1 · Agents alongside humans: how to add AI without firing people.
The thesis post. Why "AI as a tool for your existing team" beats "AI as a replacement," with concrete patterns from customer support, design, and ops. The math on productivity per person, the trust-building cycle, and how to explain it to a nervous team.
2.2 · AI customer support: replace nothing, multiply your one CS rep.
How a support agent runs alongside a single human rep, drafts every reply, looks up every order, and takes the boring 80 percent so the human handles the calls that matter. Actual numbers from our own ecom brand, plus the four kinds of tickets you should never let an agent close.
2.3 · Levels of autonomy: from observer to director, in plain English.
The 5-level ladder we use to let an agent earn more responsibility over time. Why most agents should start at Level 1 (drafts everything, sends nothing), how to know when to promote them, and what triggers a level demotion. The framework that keeps you out of trouble.
2.4 · AI CFO: not a real CFO, but a hell of an analyst.
What a CFO agent actually does (daily P&L, margin analysis, ad-efficiency flags) and does not do (sign returns, replace your accountant). The data sources, the metrics worth automating, and the moment a human CFO becomes worth hiring instead.
2.5 · The first three AI agents to build for any DTC business.
If you only had budget for three, which agents pay for themselves fastest? A field-tested ordering for DTC and ecom, with what each costs to build, what to measure on day one, what NOT to build first, and how to know when you have outgrown the starter set.
// section 3
Building with AI: the technical side.
For the technical operator, the build partner, the engineer in the room. Architecture patterns, tool choices, guardrails, and the field notes from running agents in production. Less marketing, more code.
3.1 · Building your first AI agent in two weeks.
A real timeline from blank repo to deployed agent. Day-by-day for the first week, then the second-week shape: tools, prompts, evals, guardrails, deploy. Not a tutorial, a field map. With the actual mistakes I made on the first one.
3.2 · Sub-agents and skills: how to design a multi-agent fleet.
When one agent stops being enough. How to split work across specialists, where to put the orchestrator, how to keep them from stepping on each other, and the failure mode that costs you a week if you ignore it. Lessons from running twelve of them.
3.3 · MCP servers: how AI agents connect to your stack.
The Model Context Protocol in plain English. What an MCP server is, why every serious agent build runs on one, the difference between stdio and SSE transports, and the ten MCP servers we actually use in production (Shopify, Klaviyo, Gorgias, and the rest).
3.4 · Agent guardrails and safety gates: the boring feature that pays for the build.
The single design pattern that separates "we shipped an agent" from "we kept our jobs." Where to gate, where to auto-run, how to layer approvals, and the tier system we use to keep prompt-injected agents from doing real damage.
3.5 · Why I stopped recommending LangChain to founders.
The framework war is over. Most production agents I ship today are 200 lines of Python and a Postgres table. What I reach for instead, why lightweight beats feature-complete, and the three patterns that replaced 90 percent of the abstractions.
// subscribe
Get new guides when they actually ship.
One email per post. No drip campaigns, no "here is what we are up to" newsletters, no AI-generated filler. Unsubscribe one-click.
RSS · /feed.xml · FOLLOW · @cronkagents