I recently gave a talk arguing that most companies don't actually need AI "agents" - they need workflows. The agent/workflow distinction has become one of the most confusing topics in AI engineering, since the "Building Effective Agents" blog from Anthropic:
https://www.anthropic.com/engineering/building-effective-age....
My definition: An agent is a system where the LLM controls its own execution flow based on environmental feedback. Everything else (prompt chaining, routing, parallelization, orchestrator-worker) is a workflow, even if it uses LLMs.
The problem is the line is incredibly blurry. To illustrate this, I built an interactive quiz with 7 code examples that test your intuition on what's an agent vs workflow.
The community voting results are fascinating - even technical folks disagree on edge cases like "research agents" with only 3 predefined action types, or orchestrator-worker patterns where the LLM dynamically determines task structure.
The quiz shows live voting results, so you can see how your intuitions compare to others. Would love to hear HN's take - where do you draw the line?
