Agentic AI has quickly become one of the most prominent ideas in artificial intelligence. Technology companies are building AI agents, enterprise software platforms are adding agent capabilities and organizations are beginning to explore systems that promise to perform increasingly complex work with less human involvement.

The basic idea is compelling. Instead of giving an AI system a prompt and receiving an answer, we give it an objective. The system can then determine what needs to happen, select appropriate actions, use available tools, evaluate the results and adjust its approach as it works toward that objective.

But as the term has become more popular, its meaning has also become less clear.

Some products described as “agentic AI” appear remarkably similar to something organizations have been using for years: automation. The technology may be more sophisticated and a large language model may now perform parts of the process, but adding AI to an automated workflow does not necessarily make that workflow agentic.

Understanding the difference matters because the terminology tells us something about how much decision-making authority we are actually giving these systems.

Consider a relatively simple business process. An email arrives in a shared inbox, software identifies what the message is about, extracts information from it, creates a record in another application and sends a response.

That is automation.

We can make the same process considerably more sophisticated with artificial intelligence. Instead of relying on keywords or rigid rules, an AI model could classify the email, summarize the request, extract names and dates, determine its sentiment or urgency and generate a personalized response.

The resulting workflow might look something like this:

Email received → AI classification → information extraction → record created → AI-generated response → email sent

This is certainly more intelligent than traditional rule-based automation. It may also provide substantial business value. However, if humans have still predetermined the sequence of actions, the underlying architecture remains largely an automated workflow.

A useful description for this is AI-enhanced automation.

There is nothing inherently inferior about that approach. In fact, many business processes benefit from predictable workflows. The problem arises when automation, AI-enhanced automation and agentic AI begin to be treated as interchangeable concepts.

The meaningful difference begins with who determines how an objective should be accomplished.

Traditional automation generally starts with a process. Humans determine what should happen, under which conditions it should happen and what should happen next. Software then executes those instructions.

Agentic AI moves some of that control from the workflow designer to the AI system. Instead of specifying every step, humans can increasingly define an objective, a set of available tools and the boundaries within which the system is allowed to operate.

Imagine an organization wants a system to handle routine customer billing inquiries. A traditional automated workflow might contain numerous rules describing what to do with different types of requests. If the customer asks about a duplicate charge, follow one branch. If the customer wants a receipt, follow another. If the account meets certain conditions, escalate the request.

An agentic system could approach the same problem differently. It could examine the customer's request, determine what information it needs, retrieve relevant account information, consult applicable policies, decide which available action is appropriate, perform that action and evaluate whether the customer's problem has actually been resolved.

The organization still establishes the boundaries. The AI does not receive unlimited authority. But within those boundaries, the system has meaningful discretion over how it pursues the objective.

That discretion is an important component of agency.

One reason discussions about agentic AI become confusing is that there is no perfectly clean boundary separating automation from agency. Modern systems can combine deterministic workflows, generative AI and autonomous decision-making in the same product.

It is therefore more useful to think of these technologies as existing along a spectrum:

The boundaries between these categories will inevitably overlap. A system might autonomously decide which tool to use while still operating inside a larger predetermined workflow. Another might create its own plan but require human approval before performing consequential actions.

That does not make the concept of agentic AI meaningless. It simply means that agency is better understood as a matter of degree than as a binary label.

Some of the clearest examples of this distinction can be found in the documentation of companies building agent platforms themselves.

Microsoft's Copilot Studio, for example, supports both deterministic agent flows and more autonomous agent behaviour. Agent flows can be used to create consistent, rule-based processes in which actions follow a defined sequence. Microsoft also allows agents to respond to events using instructions and available tools, giving the agent greater discretion in determining what actions should be taken.

The distinction is important because both approaches can exist within what users broadly perceive as an AI-powered system.

A trigger does not automatically create agency. Neither does connecting an LLM to an API or allowing an AI system to send an email. What matters is how much control the system has over determining the appropriate next action.

Salesforce's Agentforce provides another useful example. Salesforce describes agents as goal-oriented systems that can use available actions to complete tasks. The actions themselves provide capabilities but the more interesting agentic behaviour comes from deciding how and when those capabilities should be used.

This gets closer to the central distinction. The ability to perform an action is not necessarily agency. The ability to determine which action is appropriate in pursuit of an objective is much closer to it.

Another source of confusion is the assumption that a system becomes agentic simply because it operates without continuous human involvement.

Traditional automation has been doing that for decades.

An automated system can respond to events, call APIs, update databases, send messages, move files and operate continuously in the background without anyone pressing a button. None of those capabilities individually makes the system an AI agent.

Consider a customer support workflow that automatically receives a ticket, uses AI to classify it, retrieves information from a database, generates a response and sends that response to the customer.

The entire process could occur without human intervention. It could use a sophisticated language model at multiple stages. From the outside, it might even look like an autonomous support agent.

But if every meaningful step and decision path was predetermined by the workflow designer, its agency remains limited.

Now consider a system that receives the same support request but determines that it needs additional account information. It chooses an appropriate retrieval tool, examines previous interactions, consults the relevant policy, considers several possible resolutions, selects an action within its permitted authority, performs that action and then checks whether the issue has been resolved.

The second system has substantially more control over the path between the initial request and the desired outcome.

That is a much stronger example of agentic behaviour.

This leads to a useful test when evaluating claims about agentic AI:

Who decides what happens next?

If the answer is primarily the workflow designer, the system is probably closer to automation.

If the answer is increasingly the AI system, operating within defined constraints, the system is demonstrating greater agency.

This does not mean genuinely agentic systems should be given unrestricted control. Enterprise AI systems still require permissions, authentication, monitoring, auditability, escalation mechanisms and clear restrictions on the actions they can perform.

In fact, greater agency arguably makes those controls more important.

An organization might allow an AI agent to investigate an invoice discrepancy, retrieve records and recommend a resolution while still requiring human approval before issuing a substantial refund. The system can exercise meaningful agency during the investigation without being granted unlimited authority over the final action.

Agency and governance are not opposites. Well-designed agentic systems need both.

There is another part of the agentic AI discussion that deserves considerably more attention: not every process should become agentic.

Suppose an organization needs to retrieve information from one approved database, transform it according to a known set of rules and enter the result into another system. The process is predictable, the rules are well understood and the desired output is consistent.

There may be little reason to give an AI agent discretion over that process.

A deterministic workflow could be faster, cheaper, easier to test and easier to audit. It would also behave consistently, which can be particularly important in regulated or high-accountability environments.

Agentic systems become more interesting when the path toward an objective cannot easily be specified in advance.

Research provides a good example. An AI research agent might begin with a question, search several sources, discover that important information is missing, formulate another search, compare conflicting evidence, retrieve additional material and eventually synthesize its findings.

It would be difficult to define the exact sequence beforehand because the appropriate next action depends on what the system discovers along the way.

That is precisely where agency can provide value.

The goal should therefore not be to make every workflow agentic. It should be to determine where autonomy and adaptive decision-making actually improve the process.

There is an unavoidable commercial dimension to all of this.

“Automation” is no longer a particularly exciting technology term. Organizations have been automating processes for decades.

“Agentic AI,” on the other hand, sounds new, powerful and transformative.

That creates an obvious incentive for the technology industry to apply the label broadly. A workflow that might previously have been described as intelligent automation can become an AI agent. A chatbot connected to several tools can become agentic. A language model placed between a trigger and an action can suddenly be presented as an autonomous system.

This does not necessarily mean companies are deliberately misrepresenting their products. The terminology is still evolving, there is no universally accepted technical threshold at which a system officially becomes agentic, and many modern products genuinely combine deterministic automation with agentic components.

But it does mean customers and organizations should look beyond the label.

Calling something an “AI agent” tells us surprisingly little about what the system can actually do.

Rather than asking whether a product calls itself agentic, it is more useful to examine how it behaves.

When evaluating one of these systems, consider questions such as:

The answers provide considerably more information than the word agent on a product page.

They also help avoid the assumption that greater autonomy automatically means a better product. Sometimes the most appropriate answer to these questions will reveal that a carefully controlled AI-powered workflow is exactly what the organization needs.

The debate over what technically qualifies as an AI agent is unlikely to disappear.

There will always be edge cases. Some systems will autonomously select tools while operating inside structured workflows. Others will independently plan complex tasks but require approval before taking consequential actions. Many enterprise systems will combine automation and agency depending on the task, risk and context.

That is why the most useful question may not be:

“Is this an AI agent?”

A better question is:

“How much meaningful agency does this system actually have?”

That forces us to look beyond marketing terminology and examine where decisions are actually being made, how much freedom the AI has to determine its next action, and what happens when circumstances differ from what developers anticipated.

The future of AI will almost certainly contain plenty of automation, plenty of agents and many systems that combine the two.

Automation is not obsolete because agentic AI exists, and an automated workflow is not suddenly agentic because an LLM has been added to it.

As the term becomes more common, understanding that distinction will become increasingly important.

Because when a company tells us its product is “agentic,” the label itself is only the beginning of the conversation.