Seems like every single business has the word “agentic” somewhere on slide three of their sales deck now. 

Despite its constant use, the word does carry a real, technical definition. At the same time, the distance between products that meet it and products that borrow it is showing up in canceled contracts. 

Gartner estimates that only about 130 of the thousands of vendors selling agentic AI are building actual agents, and that over 40% of agentic projects will be canceled by the end of 2027 on cost, unclear value, and weak controls. 

The definition is not complicated, and you can apply it in a single meeting. Here are five tests for agentic AI that hold up every time.

Agentic AI has a working definition, and plenty of products miss it

Anthropic's engineering team draws the architectural line at orchestration. Workflows run through predefined code paths that a person wrote in advance. Agents direct their own process and decide their own action sequence at runtime. 

One is a program that calls a model. The other is a model that runs a program.

At Qurrent, we operate on two criteria before we’ll call anything an agent. 

1. It has to be able to take actions in a live environment and decide on its own which action to take and when

2. Then it has to see what that action produced and choose the next move based on the result. 

Miss either one and the label does not apply, no matter what sits under the hood. Meaning, this standard rules out a lot of what currently ships as (allegedly) agentic AI:

• A chat interface over your documents that answers questions and waits

• A scripted sequence with one model call sitting in the middle of it

• Screen-scraping automation with a natural-language front end bolted on

• A copilot that drafts an email, a journal entry, or a memo for someone else to send

Each of those can be useful. None chooses what happens next, and that property is what changes how a finance process runs.

5 tests that separate agentic AI from an LLM with a chat box

Run these against any product claiming agentic AI. All five are observable in a demo, and a vendor who cannot show you everyone is selling something else.

1. It picks the next action itself, so nobody has to map every path in advance

Ask who wrote the decision tree. In a scripted build, every branch was authored before the first run, so someone had to imagine every edge case up front. The real cost is in the interview, not the build: sitting with a customer enumerating what happens if the data from their ERP arrives differently than planned, then building a path for each answer.

• Scripted builds slow down at the interview stage, before anyone writes code

• Each unhandled case becomes a production exception the team absorbs by hand

• The same brittleness shows up in RPA, where bots break the first time a portal changes its layout

An agent gets a set of actions and the authority to choose among them, which is why it reaches production faster and survives contact with messy data.

2. It sees the result of its action and decides what comes next, so one bad input does not run to the end of the process

The loop is the whole mechanism of agentic AI.

The agent acts, reads what came back from the environment, and uses that to choose again. A model that generates a response and stops has no way to know whether what it produced was right.

In a payables run, the difference is concrete. A system without the loop matches an invoice to a purchase order and moves on. A system with it checks whether the match held, notices the quantity variance, pulls the receiving record, and routes an exception with the evidence attached.

3. It acts inside your systems, which means it needs governance rather than a good answer

Non-deterministic output is a property of the models, not a defect anyone will patch out. Put a second agent in an adversarial position, reviewing the first one's output against what it was supposed to do, and send the work back when it drifts.

• Deterministic components handle the math, because arithmetic should return the same result every time

• Model-driven decisions handle the nuanced calls that need judgment on real data

• A reviewing agent checks the work of the acting agent before anything is committed

Ask any vendor which parts of their build are deterministic. An honest answer names specific steps. A vague answer means the whole process is running on model output.

4. It carries what it learns forward, so your team answers the same exception once

When an agent hits something it cannot resolve, the useful behavior is to stop, ask, and record the answer for next time. That turns a one-off clarification into permanent capability instead of a recurring interruption for your controller.

Without it, autonomous AI agents generate a support queue. Your team answers the same vendor-specific question every month and the volume never falls.

5. It is measured on the process, not on the response

Response quality is the wrong unit for agentic AI. The questions that matter are whether the close finished on the first business day, how many invoices cleared without a human touch, and what a touched invoice costs you compared with an untouched one.

• Exception rate and where exceptions cluster

• Cycle time end to end, not per task

• Cost per unit of work, tracked against the baseline you started from

Ask for those numbers from a live deployment. Vendors running real agentic AI have them.

Agentic AI vs LLM features: what actually differs

Agentic AI vs LLM features are easier to settle in a procurement conversation than in an architecture diagram, because the two builds fail in different places and answer to different numbers.

The market data reflects the gap. S&P Global Market Intelligence found the share of companies abandoning most of their AI initiatives climbed from 17% to 42% in a year, with the average organization scrapping 46% of proof-of-concept projects before production. MIT's NANDA study, preliminary and contested on sample size but directionally consistent, put 95% of enterprise GenAI pilots at no measurable P&L impact. Very little of that is model capability. It is what happens when something that answers questions gets bought to run a process.

If your last automation review left you unsure which of those two you were buying, talk to us about your finance operations.

Not every process needs agentic AI, and a straight vendor will say so

Does agentic AI make scripted automation obsolete?

No, and treating it that way produces worse builds. Plenty of finance work runs well as a deterministic flow with LLM calls inside it. The sequence is fixed and programmatic, the model gets called at the specific points where a document has to be read or an exception has to be judged, and the rest of the path never varies. That is not an agentic system. Nothing is choosing what to do next.

We draws the line at two capabilities: the system decides on its own which action to take, and it sees the result of that action and decides what follows. Anything short of that is code with a language model in it, which is frequently the right build.

Is a human approval step enough of a control? 

Only if someone is actually reading it. When a process runs correctly nearly every time and a person has to approve each action, the approvals become reflexive. The one time the system proposes something wrong, the odds of it getting waved through are high. Controls have to sit where they catch the failure, not where they collect a click.

Will the terminology last? 

Probably not, and that is fine. The category vocabulary will fade the same way nobody argues about Hadoop anymore, while the behavior underneath becomes the default expectation for back-office work. Buy the behavior, not the vocabulary.

Six questions to ask any vendor selling agentic AI

Pressure-test the claim with specifics. The answers will sort agent washing from real capability within about ten minutes:

1. Which decisions does the agent make without a person, and which does it escalate? A precise boundary means they have run this in production. A general answer about autonomy means they have not.

2. Which parts of this process are deterministic code? Anyone building responsibly for accounting can name them immediately.

3. What happens when the agent is wrong? Listen for a reviewing layer and a rollback path, not for accuracy claims.

4. How does the agent learn from an exception my team resolves? If the answer involves a support ticket to them, your volume will not fall.

5. What does your SLA commit to? Uptime is not an outcome. Cycle time, exception rate, and accuracy against a defined baseline are.

6. Can I see the decision trail for a single transaction? Visibility into every step is the difference between a governed process and an opaque one.

What agentic AI has to survive to run a month-end close

Close is unforgiving in a way that demos are not. Volume arrives in a compressed window, data is inconsistent across entities, half the exceptions are genuinely ambiguous, and the output goes to an auditor who will ask how a number was derived. 

A build that handles the clean 80% and hands back the rest has moved work rather than removed it, and absorbing that remainder with people is where adding headcount stops improving quality.

The bar needs to be redefined from whether or not an agent can do the task. We need to talk about it in terms of whether or not it can own the process, including the parts nobody documented.

What agentic AI needs to clear that bar is unglamorous: deterministic components where determinism belongs, agents reviewing agents, an escalation path that codifies each answer permanently, and a decision trail an auditor can follow. We build fully managed agentic workforces against that standard, with process ownership rather than a license.

The label is free while the behavior is not

You have sat through the demos where everything works, approved a pilot, and watched it produce a queue of exceptions your team now handles on top of the original job. The word on the slide told you nothing about what you were buying, because the word costs a vendor nothing to use.

Qurrent runs finance operations end to end, with performance guarantees written into the contract and a decision trail behind every transaction. If you want to see what your own process looks like under that standard, start with a conversation about your operations.