What is agentic AI in simple terms?

An AI system that decides its own next step. You give it a goal rather than a procedure; it chooses which tools to use, executes them, looks at the result and continues until the goal is met or it runs out of budget. The loop is what makes it agentic.

What is the difference between agentic AI and an AI agent?

Agentic AI is the property; an AI agent is a system that has it. The terms are used interchangeably in practice. The more useful distinction is how far along the spectrum a system sits - from choosing one tool, to sequencing steps, to committing money on your behalf.

Is agentic AI the same as automation?

No. In workflow automation the steps are fixed in advance and a model may fill in one of them. In an agentic system the model decides what the steps are. That difference is why automation fails predictably and agents fail in ways that need monitoring.

Why do agentic systems fail more than chatbots?

Because errors compound. A chatbot that is wrong produces one wrong answer; an agent acts on its wrong answer and builds further steps on top of it. At 95 % reliability per step, a ten-step task succeeds only about 60 % of the time - which is why short loops and external verification matter more than prompt quality.

What Is Agentic AI?

Agentic AI is one of those terms that arrived fully formed in marketing material before anyone agreed what it meant. The useful definition is narrow and mechanical: a system is agentic to the extent that it chooses its own next action rather than executing a sequence someone else specified.

That is a property, not a product category, and it is a spectrum rather than a switch. Getting this right matters because the engineering requirements change sharply as you move along it.

The loop

Every agentic system is the same four steps, repeated:

Goal A stated outcome, not a stated procedure. "Reschedule this to a slot that suits both of them" rather than "open the calendar, read column three".
Decision The model selects an action from the tools available, given what it knows so far. This is the step that makes the system agentic; remove it and you have a workflow.
Action The tool is called and something in the world changes - a record is written, a message is sent, a payment is made.
Observation The result comes back, including failures, and feeds the next decision. Systems that cannot observe their own failures cannot recover from them.

The loop terminates on a definition of done, a budget, or an error the system decides it cannot handle. Systems missing the second and third stop conditions are the ones that appear in incident reports.

The spectrum, with examples

Degree	What the system decides	Example
Not agentic	Nothing. A model is called at a fixed point in a fixed pipeline.	Summarising every incoming ticket on arrival.
Tool selection	Which of several tools to call, once.	An assistant that decides whether a question needs a database lookup.
Sequencing	The order and number of steps, until the goal is met.	A research task: search, read, search again, synthesise.
Goal decomposition	What the sub-goals even are.	"Prepare everything needed for Monday's meeting."
Resource commitment	What to spend - money, obligations, other people's time.	Booking, purchasing, scheduling with third parties.

Reliability requirements rise steeply down this table, and so does the cost of being wrong. The first three rows are ordinary engineering. The last two are where the unsolved problems of the field live.

Why the loop changes the risk profile

A non-agentic system that is wrong produces one wrong output. An agentic system that is wrong produces a wrong output and then builds on it. Errors do not average out; they compound.

The arithmetic

At 95 % reliability per step, a ten-step task completes correctly about 60 % of the time. At 99 %, about 90 %. This is why practical agent engineering is mostly about making loops shorter, tools narrower and checks external - not about better prompts.

The countermeasures that actually work are unglamorous. Constrain the tool surface so the worst permitted action is survivable. Verify against something outside the model - a test, a schema, a database constraint - because a second model checking the first shares most of its blind spots. Cap the budget. Make escalation to a human a first-class outcome rather than an admission of failure.

Agentic AI versus adjacent terms

AI agent - the system itself. Agentic AI is the property it has. In practice the terms are used interchangeably and it rarely causes confusion.
Autonomous agent - emphasises operating without supervision. A system can be agentic and still supervised at every commitment.
Multi-agent system - several agents dividing a task. Adds coordination cost; often a single well-scoped agent is better engineering. Covered in the AI agents overview.
Workflow automation - the steps are fixed in advance. A model inside a fixed workflow is not an agent, and calling it one sets the wrong expectations for reliability.
AGI - unrelated. Nothing about agentic systems requires general intelligence, only reliability within a bounded task.

Where it is working commercially

Ignoring pilots and demos, deployments that survive contact with production share a shape: a bounded domain, a small tool surface, a machine-checkable definition of done, and a clean handover to a person when the task leaves that boundary. Software engineering fits because test suites verify. Customer communication fits because the escalation path is obvious and the cost of a clean handover is low - voice agents are the clearest commercial example.

Vendors and consultancies building in this area are worth reading critically, including the ones connected to this site: the company behind loob.io publishes its own material on building agent systems for business use, which is written from a commercial standpoint and should be read as such.

What would change the picture

Two developments would move agentic systems from supervised to genuinely autonomous, and neither is a model improvement. The first is verifiable delegation: a machine-readable way to state exactly what an agent may do, checkable by a third party. The second is an allocation of liability for autonomous action that businesses can insure against. Until both exist, "autonomous" in a product description means "autonomous within a fence someone drew very carefully".