How AI Agents Can Transform Your Operations Forever 

Automation promised the end of busy work, but there have been limitations to the technology. The strides made with artificial intelligence and large language models (LLMs)  are now unlocking countless possibilities for automation.  

In the past, robotic process automation (RPA) handled structured and repetitive tasks, but struggled with complexity, creativity, and nuance. It’s a drawback that has prevented leaders from outsourcing more variable assignments. Now, AI agents, which are designed to autonomously complete a particular task, have the potential to optimize business processes, workflows, and operations in unprecedented ways. 

Here’s how the technology works and how organizations can achieve even more with the guidance of AI and ML consulting partners.  

How Do AI Agents Work? 

In a nutshell, an AI agent is a program that performs a particular task with autonomy. Built on sophisticated algorithms, they can leverage LLMs, neural networks, and other machine learning concepts to process information and make decisions.  

Unlike most current LLMs, AI agents can use the internet and access APIs to complete the subtasks necessary to achieve their larger goal. Additionally, these agents have their own memory, whether that is from your proprietary data or third-party data and can use retrieval-augmented generation (RAG) to extend beyond their training model information. This allows AI agents to give more up-to-date intelligence. Their ability to collaborate with other agents or programs also enables them to respond in creative and open-ended ways that RPA cannot. 

While RPA excels at automating repetitive tasks and LLMs are powerful for text analysis, they may struggle with tasks that combine these functionalities. For instance, in the oil and gas industry, calculating revenue from a drilling pad and generating a production analysis report necessitates both text summarization and complex mathematical calculations. 

This is where AI agents come in. Unlike RPA and LLMs, AI agents can handle multifaceted tasks. They can leverage their access to external tools and services. For example, an AI agent could outsource the text generation to ChatGPT or Claude and the complex formula to a reliable equation calculator. 

While Robotic Process Automation (RPA) excels at automating repetitive tasks and Large Language Models (LLMs) are powerful for text analysis, they may struggle with tasks that combine these functionalities. For instance, in the oil and gas industry, calculating revenue from a drilling pad and generating a production analysis report necessitates both text summarization and complex mathematical calculations. 

This is where AI agents come in. Unlike RPA and LLMs, AI agents can handle multifaceted tasks. They can leverage their access to external tools and services. For example, an AI agent could outsource the text generation to ChatGPT or Claude and the complex formula to a reliable equation calculator.  

Companies that can create or access a framework of unique agents, each focused on their own specific responsibilities, will be able to automate an incredible selection of tasks that will free up  human time and resources. This is like creating a crew of subject matter experts that gain greater proficiency with tasks the more frequently they complete them.  

Use Cases for AI Agents  

What do AI agents look like in practice? There are a wide range of opportunities for this technology, and we’re in the process of experimenting with a few use cases on our own.  

For example, we have created a web of AI agents to handle things like corporate intelligence reports. A human would need to go through numerous steps to complete a report: scour search engine results, pull up SEC filings, check out their social media handles, or even dig through Glassdoor or Indeed for information. When properly trained, an AI agent can accomplish those tasks in a fraction of the time.  

Our AI agent asks itself: what is needed for a corporate intelligence report? Then, it reviews the inventory of programmatic and LLM agents it can access:  

• Do I have an agent that excels at web searches?  

• Do I have an agent that can get me information from SEC filings?  

• Do I have an agent that can compile YouTube transcripts?  

• Do I have an agent that can scrape job boards for information?  

Using the plain language documentation for each of these agents, the primary agent in this network can coordinate the efforts of the different programs to quickly create a comprehensive corporate intelligence report. From there, it’s a matter of our sales team or subject matter experts verifying the information and adjusting.  

That’s only the start. There are also a variety of industry-specific tasks which AI agents can complete:  

• In the customer service sector, AI agents can elevate the capabilities of chatbots, allowing them to leverage internal data as well as third-party APIs to verify orders, escalate issues, and answer complex questions beyond predefined responses.  

• In the healthcare space, AI agents can help combat clinician burnout by streamlining schedules, automating routine tasks, and sending out engagement surveys.  

• In the insurance industry, AI agents can be used to enhance claim fraud detection by evaluating metadata associated with fraud, identifying inconsistencies with reports, and blocking fraudulent transactions.  

• In the supply chain management space, AI agents can automate the entire workflow of optimizing inventory, increasing sales, and identifying the best time to offer discounts by analyzing market trends, customer behavior, and competitive pricing. These intelligent systems can ensure products are always in stock, reduce excess inventory, and maximize profitability using data-driven decision making. 

Using AI Agents Ethically 

As always, it’s important for your organization to implement AI agents conscientiously. Anytime you use a model, you are going to be incorporating the bias that was used to create that model into your decision making process. Sometimes, that bias can be very severe. As always, you need a human in the loop to make sure that you eliminate the biases in AI systems.  

Moreover, these LLMs might look intelligent, but it’s important to remember they are sophisticated next-word predictors. We’re not at the point of general artificial intelligence (which could pass the Turing Test). You want them to summarize data or make recommendations, but they should not make critical business decisions without a human in the loop until extensive testing has been conducted. So, whether you’re using AI in hiring processes, service decisions, rental application approval, or other essential processes, you want to make sure humans are part of the equation.  

Remember, even as you transform your operations and productivity for the better, AI agents are there to help people do their best. With AI agents, you can say goodbye to busy work and hello to better, more efficient workers.  

Want to learn more about AI agents and other artificial intelligence solutions? Check out our AI and ML consulting solutions.