How to Learn More About AI Agents: Where to Keep Up in 2026

Once you know the basics, how to learn more about AI agents becomes a question of keeping up — new models, tools and techniques land every week. The answer is a simple routine: one source for news, one community for help, the official docs for accuracy, and one new build a week. In the video below I show how agent OSs changed AI agents. Here’s my routine and the resources I use.

Best resources to learn more about AI agents

My weekly learning routine

Every day I scan what’s new — launches, updates, free models — and test the interesting ones on camera. You don’t need to go that far. A 30-minute weekly routine works: Monday, check what’s new in the tools you use. Midweek, try one new feature or model. Friday, build or improve one agent. That rhythm keeps you current without overwhelming you.

Go deeper on the core ideas

Once you’re comfortable, deepen the fundamentals: how agents use tools, how memory works, how skills are structured, how multi-agent systems divide work, and how to keep agents safe with approvals and sandboxes. The official Hermes docs, Anthropic’s courses on tool use and MCP, and LangChain Academy are great for this.

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Learn from real code and real setups

GitHub is one of the best free classrooms. When a new open-source agent launches, read its README: it explains the design, the setup and the limitations. Studying how projects like Hermes, Herald OS, StarNet or jev-ultrafast are built teaches you more about agents than most courses.

Learn with other people

The quickest way to learn more is to see what others are building. Communities let you copy working setups, ask why something failed and share your own wins. AI Money Lab is free; the AI Profit Boardroom adds four live calls a week where members show their builds.

Avoid information overload

You can’t try every new tool, and you don’t need to. Pick the two or three agents you use most, follow their updates closely, and only test new tools that solve a problem you actually have. Depth beats breadth.

How I evaluate a new AI agent

When a new agent launches, I check four things quickly. What can it actually do that my current tools can’t? Does it work with the models I use, including free ones? Is it open source or tied to one company? And can I run a real task with it in under an hour? If the answers are good, I test it properly on camera. That filter saves a lot of time and is worth copying.

Turn learning into results

Learning more only matters if it changes what your agents do. After every new technique you learn, ask: which of my agents would this improve? Then update one agent that week. That habit turns a stream of news into a steadily better system.

Where I’d point an intermediate learner

If you already run a few agents, the biggest gains usually come from three areas: better skills (clear steps, examples, checks), better memory (accurate, organised and reviewed), and better orchestration (sub-agents and schedules that work together). Study how experienced builders structure those three things, and your agents will become noticeably more reliable within a few weeks.

Building a personal agent playbook

Keep a simple notes file — or a folder in Obsidian — with what you’ve learned: the prompts and skills that worked, the models that suit each job, the mistakes that cost you time, and links to the docs you use most. Review it monthly. Over time it becomes your own playbook, and because agents like Hermes can read your notes, it makes your agents smarter too.

Keep learning more about AI agents

That’s how to learn more about AI agents: a weekly routine, the official docs, real code and a community. Join AI Money Lab free, follow my YouTube for new agents as they launch, and join the AI Profit Boardroom for live calls and the Agent OS.

The most important habit is consistency: thirty minutes every week, without fail, beats an occasional weekend binge. AI agents reward people who keep up steadily, because each small improvement builds on the last. Set a recurring reminder, protect that time, and use it to try one new thing and improve one existing agent. Within a few months you’ll be the person others ask for advice, and your agents will be doing far more of your work. If you want a head start, join a community where people share what’s working each week; you’ll learn from dozens of experiments instead of only your own.

About Julian Goldie

Julian Goldie is the founder of Goldie Agency, the best-selling author of Link Building Mastery and one of the most-watched AI educators on YouTube, with 430K+ subscribers. He tests new AI agents almost every day and teaches them in the AI Profit Boardroom and the free AI Money Lab community.

FAQ: how to learn more about ai agents

How can I learn more about AI agents?

Follow one news source, join a community, read the official docs and build one new thing a week.

What should I learn after the basics of AI agents?

MCP, browser and computer use, multi-agent systems, memory providers, safety and running agents 24/7.

Where can I keep up with new AI agents?

Julian Goldie’s YouTube channel covers new agents almost daily.

Are GitHub repos good for learning AI agents?

Yes — READMEs and code show how real agents are designed.

Is there a free community for learning AI agents?

Yes — AI Money Lab.

Related reading

• Best AI Agent Course 2026
• How to Learn AI Agents
• Hermes Agent by Nous Research

Last updated October 2026. This page is a living guide to how to learn more about ai agents — the facts here move fast and it is updated as they do.