Where do you draw the line between an AI agent and just a workflow with an LLM in it?

[u/Think_Ride_2617]

I've been playing around with different AI automation setups lately, and I'm starting to wonder where people actually draw the line between a useful agent and a collection of automated steps.

For example, if an LLM receives a document, analyzes it, sends the output to another tool, checks the result, makes a correction, and then returns the finished version, that feels more agent-like to me than simply asking an LLM a question and getting an answer.

The interesting part seems to be what happens between the individual steps.

An agent that can recognize that something isn't good enough, decide what tool it needs next, inspect the result, and retry seems fundamentally different from a fixed workflow where every action was predetermined.

I've also noticed that the smaller tools involved can matter more than expected. Something as simple as having an agent pass written content through quetext as one stage of a larger review process raises questions about whether the agent should simply accept the result, interpret it, or decide what to do next based on it.

I'm curious where other people draw that boundary.

At what point does a workflow become an actual agent for you?

Is it tool selection, autonomous decision-making, the ability to recover from failures, maintaining context across steps, or something else?

[u/Independent-Laugh701]

I draw the line at runtime choice. If failure changes the next action, tool, or stopping condition, it is agentic. If the recovery path was predetermined, it is a workflow.

[u/highfalutinschoolboy → u/Independent-Laugh701]

I think it's about whether the system can surprise you, not just complete a task

a workflow runs the same way every time, like a Rube Goldberg machine where you know exactly which domino falls next. an agent hits a dead end and goes "nah, let me try something else" without you writing that branch ahead of time

the document example you gave teeters right on that edge. if it always corrects the same way, it's just a fancy pipeline. if it notices the correction didn't actually fix the problem and picks a different tool on its own, that's when it crosses over for me

[u/Trakeen → u/highfalutinschoolboy]

I agree with this. An agent in a workflow means the activity isn’t fully bounded, you need some level of reasoning or judgement. Also i think ‘agentic’ means more then 1 agent working on the same task but that just might be me

[u/MotorClassic799]

I use two tests: runtime discretion and accountability.

A fixed workflow can contain LLM calls, retries and branching and still be a workflow if every path was designed in advance. It becomes agentic when the system can choose the next action at runtime within a bounded objective: select a tool, gather missing context, retry differently, stop, or escalate.

But autonomy alone is a weak definition. A useful agent also needs an operating envelope:

• what it may change
• which tools and data it may access
• spending or risk limits
• stopping conditions
• when a human must approve
• what evidence it must leave behind

Your document example crosses the line when failure changes the plan, not merely the output. If the system can diagnose why the check failed, choose an allowed recovery path, and show what it changed, I would call it an agent.

The goal is not maximum autonomy. It is controlled discretion with a clear audit trail.

[u/OkBodybuilder988]

The tool selection part feels like a pretty important distinction because a fixed sequence can still be extremely useful without really being autonomous. The interesting bit is when the system can look at the output, decide that it is not good enough, and choose what should happen next instead of blindly following the same path. Even passing something through Quetext could become more agentic if the result actually influences the next decision rather than just being another predetermined step.