How should I choose between Agentic Automation, Document Understanding, AI Center, and Communications Mining?

Agentic Automation How to Use Agentic Automation
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Recently, I’ve noticed a strong buzz around Agentic Automation, and I’m personally very excited about its potential.

At the same time, I’m finding it difficult to understand when to use Agentic Automation versus other existing AI solutions from UiPath—namely, Document Understanding, AI Center, and Communications Mining.

For example, when planning a new automation solution, I often find myself wondering:

  • In what types of scenarios is Agentic Automation the recommended approach?
  • For documents, I understand that Document Understanding is still the go-to solution.
  • In other cases—like analyzing and classifying emails—should I still use Communications Mining, or is that now something Agentic Automation can handle just as well?
  • When it comes to AI Center and traditional machine learning models—are they still necessary?
    I assume Agentic Automation (based on LLMs) is not a complete replacement or “superior version” of ML, but the line is getting blurry.
    I’d like to understand what kinds of tasks still truly require ML models built and deployed via AI Center, and where Agentic Automation is now a better fit.

I feel these kinds of situations will become more common as the platform evolves.

In particular, I would love guidance on the following:

  • In what types of scenarios is Agentic Automation the recommended approach?
  • For documents, I understand that Document Understanding is still the go-to solution.
  • But in other cases—like analyzing and classifying emails—should I still use Communications Mining, or is that now something Agentic Automation can handle just as well?

If there are any official recommendations or best practices from UiPath on how to choose between these tools, I would really appreciate your insights.

Thank you in advance!

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Hi @mkt.scott4
Great questions :clap: I think many of us are asking the same as Agentic Automation becomes more prominent.
Here’s how I personally look at it:
Agentic Automation is best suited for scenarios where the automation involves multiple decision points, interactions across systems, and even human-in-the-loop input. Think of it as ideal for “grey area” use cases where we’re not just automating steps, but actually navigating context, making decisions, and adapting dynamically. It shines in high level coordination and orchestration tasks.

In contrast, tools like DU remain the go to for well scoped document extraction. its about mimicking human reading not decision making. It’s precise, efficient, and doesn’t need the complexity of a reasoning agent.

When it comes to Communications Mining (IXP) :wink:, it’s still the better choice when your goal is straightforward email or message classification especially if you need fast setup and lower compute costs. agentic automation can do similar tasks, but IXP often wins in terms of speed, simplicity, and efficiency.

As for AI Center and traditional ML models: yes, they’re still very much relevant. For scenarios that require highly accurate (we can’t trust gpts 100% :sweat_smile:), explainable, and fine tuned models (e.g. risk classification, forecasting), ML remains critical. Agentic Automation doesn’t replace ML it complements it.

In fact, the real power comes from combining these tools for ex:

  • Use DU to extract data from financial documents.
  • Then pass it to an Agent to generate a report, ask for human review, and send an email.
  • Use IXP to triage incoming messages before passing them to agents for contextual processing.

So it’s less about “Agentic vs AI Center or DU” and more about the right tool for each layer of the process.

Hope this helps clarify things a bit :blush:

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