Defining Agentic AI – Limitations and Opportunities

Agentic AI is the newest trending development that has captured the imagination of businesses around the world. Promising genuine autonomy, it’s often been referred to as the next step beyond generative AI. It can make decisions, perform tasks, and learn tasks independently as needed for it to achieve its goals. On paper, agentic AI appears to take us one step closer to unlocking the full potential of artificial intelligence. But in an industry saturated by marketing and hype, has it lived up to its reputation?

 

Like all emerging tech, agentic AI’s actual capabilities may differ from its reported strengths. This guide unpacks what automated AI agents are, what they can do, their limitations and how they relate to specialised AI solutions, like Xtracta’s data extraction software.

 

What Agentic AI Claims to Be

Agentic AI is designed to mimic human initiative. Unlike generative AI, which focuses on producing content or responding to prompts, agentic AI simulates goal-oriented behaviour. Rather than suggesting the next steps, it takes action and completes tasks on behalf of humans. Here’s how it’s supposed to work, in theory:

 

Five Levels of Agentic AI

1. Perceive: Agentic systems process data from a variety of sources, including databases, sensors, applications, and even natural language prompts.
2. Reason: Next, the agent evaluates context. By analysing the situation, it makes sense of what the data means and where it is relevant.
3. Decide: With the data now contextualised, agentic AI decides on the best course of action.
4. Implement: Through APIs and even mimicking user interface interactions as a human would interact with them, the agent can interface with external tools and systems to make decisions. Common industry initiatives with such agents often see them performing tasks such as rescheduling deliveries, processing refunds, or initiating the opening of support tickets. Safeguards may be put in place to prevent misuse, like requiring approval before transferring large sums in the case of refunds.
5. Learn: Using reinforcement learning, agents may adjust their behaviour based on outcomes.

 

The technology combines:

• Autonomy: Operating without step-by-step instructions.
• Goal-setting: Breaking down objectives into tasks.
• Adaptive learning: Refining performance over time.

 

In theory, it’s the difference between an assistant who drafts your itinerary and one who books it and rebooks it if your flight is delayed.

 

The Limitations of Agentic AI Use Cases

While this does sound impressive, how agentic AI performs in real-world scenarios often falls short. Much of what’s currently labelled as agentic AI is essentially workflow automation, just with more complexity and an alluring name. Most implementations are highly constrained, reliant on brittle integrations, and fail as soon as something unexpected happens. In many cases, the “agent” is an elaborate script wrapped in an LLM and a mixture of APIs.

 

True autonomous AI remains aspirational. Until foundational limitations are solved, agentic systems are unlikely to outperform more targeted, specialised AI in real-world environments. Some of the reasons why include:

 

• Demonstrations are deceptive: Agentic AI often performs well in tightly controlled scenarios but breaks down in unfamiliar contexts where the underlying LLMs that the agent relies on cannot meet the needs of the process. For example, in the automated document data extraction use case, extracting data documents may work for certain templates and document types, but other document types and tricky layouts may lead to errors for General AI models.
• Human effort is hidden: Many agentic workflows still rely on a person working behind the scenes – tweaking inputs, maintaining data pipelines, or resolving edge cases.
• Ethical and operational risks: In high-stakes settings like healthcare or large-scale operations, errors from autonomous decisions can be costly or even dangerous.

 

Specialist AI – A Tailored Solution

With the current state of Agentic AI and the LLMs that underpin them, organisations are realising that autonomous agents that attempt to do tasks on their behalf may struggle with more difficult use cases. As such, many organisations are continuing to implement specialist AI systems as these are where the most productivity gains are currently being made. These tools may not capture headlines or attract viral attention, but they deliver results quietly behind the scenes. Specialist AI is less glamorous but more robust than an Agent attempting the same tasks using its underlying general AI LLM.

 

That being said, a hybrid is emerging where agents can interface with specialist AI for more difficult parts of their process. For example, for document data extraction, an agent may perform a range of logical tasks needed but call upon the APIs of a specialist intelligent document processing (IDP) system, such as Xtracta, to perform document data extraction tasks that require high accuracy and precision, rather than using the underlying Agent’s LLM to do this.

 

Let’s use invoice processing as an example.

 

>Automated Invoice Processing Using Xtracta

Xtracta’s invoice processing AI models are trained on all forms of invoices. For most documents, regardless of format or template, data is accurately identified and captured based on what you ask it to find. From PDFs to JPGs, an AI-powered invoice capture system saves hours of time by processing these documents for you. This system can be integrated as part of an Agentic process, whereby accurate document data extraction by Xtracta’s specialist AI models can be incorporated inside the agent.

 

Manually sorting and verifying invoices can be fully automated with minimal setup. Already trained, our specialist AI API lets your business become paperless, streamlined, and well-positioned to beat the competition. Specialist doesn’t mean inflexible, either. Xtracta’s API seamlessly integrates with the software you already use.

 

Using Agentic AI Responsibly

There’s no denying that the concept behind agentic AI is powerful. In theory, autonomous agents that understand your business goals and manage tasks across systems could save time and money. With that said, in its current form and for the perceptions of many users trialling the technology, agentic AI resembles elaborate macros more than intelligent workers. Although this will change over time as users become more familiar with preparing agents and their underlying technology improves.

 

For businesses trying to scale, boost productivity, or save money, agentic AI must be carefully deployed. Mistakes in its deployment and operations could lead to unreliability and inaccuracies, which are a liability rather than a competitive advantage.

 

The most value that AI has to offer businesses, especially those dealing with volume, is precision and consistency – qualities that specialist AI systems already excel in. If you need results immediately, investing in specialised AI tools compatible with your workflow is best. If you’re curious about agentic AI, treat it as an experiment in low-stakes areas and integrate specialised AI where precision is needed. If you’re told it’s just like a person, you may want to think again.

 

Looking for Solutions Beyond AI Trends?

In a digital world saturated by marketing, agentic AI makes an attractive promise it often cannot keep. Broader doesn’t mean better in this case. General AI models cannot solve all business cases and problems. Specialised AI that is trained to do specific tasks well—even when faced with new information—is where the most value lies for businesses today.

 

For more information about how Xtracta can help your business find automation opportunities and make productivity gains, get in touch with our experts today.