Agentic AI Does Not Necessarily Mean Autonomous AI

One of the recurring debates on our project is surprisingly simple:

“If the system is not fully autonomous, can we still call it agentic?”

For a long time, I assumed that agentic AI and autonomous AI were essentially the same thing. If a human remained in the loop, I would have been hesitant to call the system an AI agent.

However, I recently came across a definition from Andrew Ng that offers a more useful perspective.

According to him, an agentic workflow is a process where an LLM-based application executes multiple steps to complete a task.

The key idea is not autonomy.

The key idea is workflow.

A traditional LLM interaction looks like this:

Prompt → Response

For example:

“Write an essay about topic X.”

The model generates the answer in a single pass.

An agentic workflow looks different:

Plan → Research → Draft → Review → Revise → Final Output

Some of these steps may involve web search, APIs, databases, external tools, or even human review.

This means a workflow can still be agentic even if a human participates in the process.

A useful distinction is:

Agentic = multi-step reasoning and execution

Autonomous = operating with little or no human intervention

All autonomous systems are agentic.

Not all agentic systems are autonomous.

This distinction is particularly important because many real-world enterprise systems intentionally keep humans in the loop for quality assurance, compliance, risk management, or decision-making.

In fact, Andrew Ng continues this discussion by introducing the idea that agentic systems can exist on a spectrum. Some workflows are highly autonomous, while others involve frequent human intervention. Both can still be considered agentic.

What I found particularly interesting is that many product and delivery processes already follow an agentic pattern today.

A typical product workflow might look like this:

Requirements → Analysis → Design → Implementation → Testing → Review → Release

The workflow itself is iterative, multi-step, and involves reflection, validation, and revision.

The difference is that humans execute most of these steps.

Agentic AI applies the same principle but delegates some of the work to LLMs and tools.

This also highlights what may be the most important skill in building agentic systems: task decomposition.

The challenge is rarely the model itself.

The challenge is breaking a complex objective into smaller executable steps, deciding which actor should perform each step, and orchestrating the overall workflow.

In that sense, product managers, delivery leads, conversation designers, and solution architects have been designing agentic workflows for years.

The actors are simply changing.

Agentic AI vs. Autonomous AI:

Agency Exists on a Spectrum

One of the ideas I found particularly useful is Andrew Ng’s argument that agency should not be treated as a binary concept.

The AI community has spent years debating whether a particular system qualifies as a “real agent”. Andrew Ng argues that this debate is largely unproductive. Instead of asking whether a system is or is not an agent, we should ask how agentic it is.

Some systems follow a fixed sequence of predefined steps. Others allow the LLM to decide which tools to use, what information to retrieve, and what actions to take next. Both can be considered agentic, but they operate at different degrees of autonomy.

This distinction is especially relevant in enterprise environments. Many of the most valuable AI systems today are not fully autonomous. They intentionally keep humans in the loop for governance, compliance, quality assurance, and risk management.

In practice, the most important architectural question is often not whether a system is agentic, but which decisions are made by the human, which are delegated to the LLM, and which are encoded directly into the workflow.