Most devs don't understand what agents are

This post is auto-generated from a public YouTube video for personal study notes. It is not an endorsement. The transcript is machine-derived and may contain errors; refer to the original video for accuracy.

About this video

When OpenAI announced AgentKit, they called it a tool for building agents. But looking at their examples, what they're showing are deterministic workflows with predetermined steps—not true agents.

This matters because agents and workflows are fundamentally different patterns for solving different problems.

In this video, I break down: - What actually makes something an agent vs. a workflow - Why Anthropic's definition (from their "Building Effective Agents" article) has become the standard - The key difference: who decides when to stop—the LLM or your code? - When to use agents (unclear paths, need for improvisation) - When to use workflows (known solutions, need for optimization) - Why most real systems exist on a spectrum between the two

Agents are like jazz—improvisation and feel. Workflows are like classical music—optimized upfront for perfect execution.

Both have their place, but mixing up the terminology makes it harder to communicate about the trade-offs.

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