As the volume of data continues to grow, businesses are looking for solutions to process it efficiently and make sound decisions. Agentic LLMs (Large Language Models) are a promising approach to automating these tasks. In this article, we explore how these AI models can be used in practice to optimise processes and speed up data-driven decision-making.

What Are Agentic LLMs?

Agentic LLMs go beyond text processing. They are advanced AI systems designed to operate autonomously and take independent actions based on the instructions or goals they are given. Unlike traditional LLMs, which primarily generate text in response to user queries, agentic LLMs are equipped with the capability to perform autonomous tasks, interact with environments, and make decisions without requiring continuous human input.

More precisely, an agentic LLM system is capable of autonomously performing tasks such as the following:

• Do a complex query to a relational DB

• Make a calculation with an external calculator program

• Get data from the Internet and use it to take decisions or generate a text response

• Research among a big data source (like for example Google Big Query, which contains global information about  
  Google searches and users)

• Query a Vector Store (and act like a RAG system, see [reference here])

• Ask a question to another specialised LLM and decide which action to take next

• Etc.

Also, these actions can be chained into complex workflows aggregating the different agents into a very smart “cooperating” system.

How Agentic LLMs Improve Data Processing

Traditionally, processing and analysing vast datasets required manual effort and significant time. Agentic LLMs help automate and streamline these processes, reducing the need for manual intervention. They can pull data from various sources, analyse it in real-time, and perform complex assessments that would otherwise take days or even weeks.

For instance, in manufacturing, agentic LLMs monitor machine data, detecting patterns that could indicate potential failures. By analysing this data, they allow companies to plan maintenance in advance, minimising costly downtime and improving operational efficiency.

Agentic LLMs also play a critical role in integrating data from different sectors, such as the Internet of Things (IoT). By processing sensor data in real-time, these models enable machine-to-machine communication, optimising processes without the need for human intervention.

Applications in Decision-Making

Agentic LLMs provide businesses with an efficient way to automate decision-making. They go beyond standard algorithms, offering flexible decision-making processes based on real-time data analysis. This adaptability makes them valuable across a range of industries.

In finance, for example, agentic LLMs can process vast quantities of market data, identifying trends that human analysts may overlook. Using these insights, they can autonomously execute investment strategies or manage risk assessments, allowing businesses to act faster and more efficiently.

In research and development, agentic LLMs offer new opportunities by identifying data patterns that might have been previously overlooked. Also, it is possible to query large amounts of research data without the need of reading one paper at a time, speeding the process and optimising the effort.In the pharmaceutical industry, for example, these models are used to speed up the drug discovery process by analysing molecular data and identifying potential candidates more quickly than traditional methods.

Automating Complex Workflows with Agentic LLMs

One of the key strengths of agentic LLMs is their ability to automate complex workflows that typically require human oversight. These models can handle tasks more quickly and accurately, making them valuable in industries ranging from manufacturing to logistics and healthcare.

In logistics, agentic LLMs can analyse transport data in real-time, optimising supply chains by making instant decisions to adjust routes and deliveries. This not only improves efficiency but also reduces costs and enhances the reliability of the supply chain.

In healthcare, agentic LLMs help automate patient data analysis, enabling the creation of personalised treatment plans tailored to individual patient histories. This improves patient outcomes while reducing the burden on healthcare professionals.

Challenges and Considerations in Implementation

Despite their potential, the implementation of agentic LLMs comes with challenges. One of the main obstacles is ensuring the quality of the data being processed. Poor-quality or incomplete data can lead to inaccurate predictions and decisions, undermining the effectiveness of these models.

Another significant consideration is ethics. Businesses must carefully monitor agentic LLMs to ensure they are not making biassed decisions that could unfairly impact certain groups. Additionally, strict adherence to data privacy regulations is essential to protect sensitive information and maintain trust.

Conclusion

Agentic LLMs are an effective tool for businesses looking to streamline data processing and decision-making. Their ability to automate complex workflows and analyse data in real-time makes them a valuable asset across a range of industries. While challenges exist in implementing these models, the benefits they offer in terms of efficiency and productivity are considerable. As more businesses adopt agentic LLMs, these AI models are set to play a central role in shaping the future of data-driven decision-making and process optimisation.