In a recent LinkedIn post, Alvin Huang discusses why founders should look beyond using AI to answer questions and learn how to build agents that can take on tasks. This article covers Huang’s post and examines his case for practical, accessible training; it is journalistic coverage of his ideas, not an article written by Huang.
“Answering questions is the least useful thing AI can do”
Huang argues that AI’s business value grows when it moves from responding to prompts to carrying out work. He describes agents as a way to shift routine tasks off a founder’s plate, while emphasizing that people need enough understanding to guide their development and assess whether they are working well.
From AI assistance to task ownership
In Huang’s view, using AI for an individual task may save time, but an agent could take responsibility for handling a task more independently. He frames that distinction as a practical opportunity for founders considering how AI might change their operations.
“Using AI saves you time on a task. Building agents takes the task off your plate entirely.”
The claim points to a broader shift in how businesses might use AI: from tools that support an employee’s workflow to systems designed to complete defined pieces of work. Huang does not suggest that founders need to become engineers. Instead, he argues that basic familiarity helps them brief technical staff and judge an agent’s performance.
Six free courses for learning agent basics
To make the subject approachable, Huang highlights six free or free-to-audit learning options. The programs span introductory material, hands-on exercises, agent design patterns, tool use and connections to business systems. His list includes:
- Hugging Face’s AI Agents Course, with framework lessons, challenges and a certificate.
- Microsoft’s AI Agents for Beginners, offering short lessons, videos and code samples.
- Google and Kaggle’s 5-Day AI Agents Intensive, covering tools and context.
- DeepLearning.AI’s Agentic AI course, which addresses planning, reflection and multi-agent workflows.
- Anthropic Academy’s introduction to the Model Context Protocol, focused on connecting agents with tools and data.
- A second Google and Kaggle intensive on building agents through natural-language instructions.
According to Huang, some agent training is aimed at engineers, but founders can still follow these courses themselves or pass them along to a technical team member. He presents the options as a way to move from general interest toward informed experimentation.
Why understanding matters to founders
Huang argues that familiarity with agents can help founders give clearer direction and evaluate whether a system is doing its job. That perspective places learning ahead of adoption: before delegating work to an agent, a business leader should understand enough to define the task and assess the result.
“Without that understanding, it’s hard to brief a developer or judge whether an agent is doing the job well.”
Huang closes by asking readers what work they would want an agent to handle. The question brings his argument back to operational priorities: the useful starting point, in his framing, is a real task that could be taken off a team’s hands.
📝 About This Content
This article is based on insights shared by Alvin Huang on LinkedIn.
📅 Originally posted on November 4, 2026 | View original post on LinkedIn →