Navigating the AI question: Understanding the difference between AI agents and tax engines
by May 29, 2026
In the world of tax technology, we’ve been trained to love rules. We thrive on precision, logic, and the comfort of knowing that if you input data into a tax engine, it will spit out the same calculation every single time. It’s a world of if-this-then-that.
But as LLMs and AI agents enter our workflows, we are facing a fundamental shift. Many tax professionals are treating these new tools as if they are just upgraded tax engines: faster, smarter, and more flexible. That assumption is dangerous. If we want to harness AI without falling victim to the illusion of control, we have to change how we talk to, test, and trust these systems.
To help explain how AI should be used in tax compliance, we asked an expert from our parent company, Stripe, to help simplify these concepts. Aleksandra Bal is the Global Indirect Tax Technology Lead at Stripe, leading a team focused on developing tax technology solutions and managing indirect taxes across six continents.
Not all AI tools are the same
Before a tax team can evaluate whether AI belongs in their stack, it helps to understand what they’re actually being offered. AI has become an umbrella term that covers very different tools, and the differences matter.
A workflow is traditional rule-based automation. It follows a fixed sequence of steps and will always produce the same output given the same input. There’s no reasoning involved. This is precisely why workflows are so well-suited to tax: they’re auditable, predictable, and traceable. If something goes wrong, you can find the exact step where the rule was applied. Their limitation is rigidity, the moment a process deviates from the predefined pattern, a workflow cannot adapt.
An AI workflow looks similar from the outside. The steps are still fixed, but one step uses an AI model to interpret something messy: a scanned attachment, a free-text product description, an ambiguous invoice line. The intelligence is contained inside that single step and doesn’t influence the rest of the process. For many tax functions, this is actually the sweet spot: you add flexibility where it’s needed without sacrificing the predictability the rest of the work demands.
An AI agent is something different. Rather than answering a question, it works toward a goal. It can break that goal into steps, choose tools, consult documents, and decide what to do next, all without being explicitly instructed. Each run may follow a different path because the system is reasoning in real time. That’s what makes agents powerful. It’s also what makes them unpredictable.
Understanding which tool you’re actually evaluating is the first step to using it responsibly.
A different kind of thinking: From deterministic to probabilistic
Once you understand the spectrum of tools, the biggest conceptual hurdle becomes clear: moving from a deterministic mindset to a probabilistic one.
Traditional tax software is like a recipe: follow the steps exactly, and you get the same cake every time. An AI agent, however, is more like briefing a colleague. You give them a goal and some resources, and you let them decide the path. Two different colleagues might take two different routes to the same answer, and an AI agent might take a different route every time you hit enter.
In tax, variation is usually seen as a bug. In AI, variation is a feature of how the technology works. When an agent gives a poor answer, it’s rarely a coding error you can just patch. Usually, it’s a sign that the instructions were vague or the context was incomplete.
This shift also changes how you validate AI systems. Traditional tax software is tested — you ask whether it passes or fails against a defined expected output. Agents can’t be tested the same way, because there may be several acceptable answers to the same question. Instead, teams move from testing to evaluation: assessing reliability, quality, and risk across a range of outputs. The goal isn’t to eliminate variation. It’s to decide whether the variation is acceptable for the task at hand.
Getting the fit right: When agents help and when they hurt
Once a tax team understands what an agent actually is, the next question is whether a given task is a good match for one. Three practical questions cut through most of the uncertainty.
- Is the workflow rule-based or interpretation-heavy? Some tax tasks are rigid from end to end, think electronic VAT filing, where every field has an expected format and every number has a defined place. Introducing an agent to decide what to populate adds complexity without adding value. A deterministic system handles this more reliably because predictability, not flexibility, is what matters.
- Can you tolerate variation in the output? Even well-designed agents produce slightly different answers from one run to the next, and that’s not a flaw, it’s how probabilistic systems behave. For research tasks, the aim is to surface possible interpretations and support human judgment, so some variation is harmless. Core compliance is different. Tax calculation, rate determination, and return preparation must withstand audit scrutiny and depend on stable, predictable logic. An agent’s adaptive behavior conflicts directly with that need.
- Does the task involve data volumes or patterns that are hard to capture with fixed rules? Some tax workflows don’t fail because the rules are unclear — they fail because there are simply too many records to review and too many ways those records can deviate from expectations. Anomaly detection is a good example: flagging transactions with inconsistent VAT treatments or outliers in recovery claims across thousands of records. When the job is to find irregularities at scale, a probabilistic system can be exactly the right tool, provided the human stays in the loop.
The role of AI in compliance
The most important question for any tax team isn’t “Can we use AI for this?” It’s “Do we want a system that follows our rules, or one that decides how to reach a goal?”
AI tools aren’t smarter tax engines. They are entirely different animals. Understanding their limitations isn’t optional, it’s the only way to ensure that when the AI invents a tax rule, you aren’t the one filing returns based on it.
We are often asked how AI will change this space. At Stripe and TaxJar, we believe AI is a transformative tool, but it isn’t a silver bullet.
- Where AI wins: AI is incredible at processing vast amounts of data, identifying patterns, and automating workflows. It helps us monitor hundreds of thousands of tax rate changes and classifications.
- Why human experts are essential: While AI is great at monitoring tax changes, it’s not a perfect system. Often, AI can’t parse through tax legislation or understand what changes are current and which are previous rulings. AI can’t navigate the nuance of a complex audit or interpret the spirit of a new, ambiguous piece of legislation.
We see the future as a partnership between human expertise and machine intelligence. We’ll continue to evolve and use AI to support our workflows, but our team of tax experts remains the final line of defense, ensuring that the precision your business depends on is never compromised.
One of the best ways to manage compliance continues to be using tax software, like TaxJar or Stripe Tax. Consider signing up for a free TaxJar trial or reaching out to the sales team. See how TaxJar can transform the challenge of sales tax management into a seamless part of your business operations.
Aleksadra holds a Ph.D., MBA, LLM, and several other degrees in taxation and computer science. She’s a contributing author to Bloomberg Tax, among other publications.
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