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The AI space is evolving fast. We went from simple Natural Language Processors like GPT-3 to AI agent systems capable of performing complex cognitive tasks within a few years. These agents are changing how organizations operate and are set to be the next big thing in tech and business.
In simple terms, AI agents are software programs that can learn, think, and act on their own without the need for human input. In other words, it’s like having a personal assistant who never sleeps, never gets tired, and can handle an endless list of tasks on their own.
Read on to learn more about these systems and their various applications in business and fintech. We’ll explore everything from what AI agents are to AI agents for business and provide examples of some of the best AI agents for your consideration.
An AI agent is an AI-backed software designed to accomplish various tasks in specific or multiple niches. Most agents are industry-specific and complete tasks unique to their field.
However, some agents have functionalities encompassing a broad range of tasks, including:
These agents act autonomously, collecting data from their environments and making decisions based on this data to accomplish complex tasks. They learn from these interactions and experiences to refine their functionality and performance.
While still a novel technology, generative AI agents have potential widespread applications in business, healthcare, customer services, and other facets of modern society.
The most notable benefits of utilizing artificial intelligent agents are:
Agents take a huge load off repetitive assignments that require human effort by automating them. That way, you can focus on more productive and strategic initiatives while these agents handle the exerting and time-consuming jobs.
For instance, they can handle customer questions and give appropriate feedback in real-time instead of involving a human customer support representative.
An example is warehouses that use sorters, AR/RS, and other agents to sort, pack, and transport goods, cutting “walking” time by up to 40%[1].
Organizations can use artificial intelligence agents to identify bottlenecks in their operations and address them accordingly. A good example is using RNN agents to predict market demand[2]and avoid resource wastage. These agents can also track workflows from the start to their conclusions and eliminate inefficient processes for better productivity.
Gut-feeling decisions are slowly becoming a thing of the past thanks to the innovation of artificial intelligent systems. C-suite employees and business owners can use agents to make data-driven decisions. These agents can analyze tremendous amounts of data from various sources and gain critical insights to inform your decisions.
When most people think of cost-cutting with AI, they think about replacing workers with machines, but this is far from the truth. Generative AI agents aren’t replacements for workers but rather their personal digital assistants. These systems reduce operational costs by eliminating wastage, ensuring the most feasible resource allocation, and ensuring efficiency across the board.
Scalability is a major concern for businesses[3], especially start-ups looking to grow their reach and expand their customer base. Agents make scalability a breeze by eliminating the need to expand the workforce as the business grows.
Unlike human staff, these agents don’t require offices, monthly salaries, or costly training for expansion operations. Furthermore, if profits dwindle and you need to downscale, you can simply stop using some agents instead of dealing with complicated layoffs and restructuring.
Different AI technologies are used in different artificial intelligence agents, but all agents share the same architecture. This architecture is the foundational basis of these systems and comprises three main components, namely:
Architecture describes the fundamental base of artificial intelligence agents. This could be physical electro-mechanical structures, as is the case with automated machinery and robots, software like LLM-based agents[4], or a combination of the two (think robot waiters).
This is the execution of commands by the agents, translating data into actions to accomplish their required tasks. Primary considerations in agent function include the agent’s database, feedback mechanism, information needed, and similar technologies.
Once the developers have the architecture and agent function on lock, the final step is to develop the agent program. The agent program is the actual implementation of the agent function. It means putting the AI agent to work, which typically involves developing, training, and testing the agent to ensure it aligns with its technical requirements, performance capacity, and business logic.
Most agents are based on Large Language Models (LLMs):
AI systems capable of understanding and generating language at a human level. These LLMs use complex algorithms and deep-learning techniques to analyze vast amounts of data and identify relationships between words and phrases. This allows them to mimic actual human languages, including contextual and expressive nuances.
That said, not all agents are LLM-based. Some agents use simpler technologies or a combination of different technologies to achieve their bottom lines.
Read more: LLM Customization: Advantages and Techniques
Some of these technologies include:
1. Rule-based systems: With rule-based systems, agents follow a pre-defined set of rules and conditions that the programmer creates. These systems are simple and inflexible but the most feasible option for accomplishing mundane tasks.
2. Neural networks: Neural networks are a step above conventional LLMs. These are technologies and software that teach AI systems to interpret and process data like the human brain. The most notable neural networks are:
3. Robotic systems: Robotic artificial intelligence agents use sensors and computer vision to interact with the physical environment using control theory principles [5].
4. Evolutionary algorithms and genetic programming: This mimics Darwin’s natural selection and the evolution process to find the “fittest offspring” to create the next generation of agents. Genetic programming [6]is still in its infancy but has created a huge buzz in the tech community.
5. Multi-agent systems: As the name implies, this technology uses multiple agents that interact with each other and operate as a holistic system to accomplish collective goals.
There are several different types of agents based on their underlying technologies, applications, and capabilities. All agents can be broadly classified into the following types:
These are the most rudimentary types of agents that follow condition-based rules. They don’t refer to data from the past to make decisions but only focus on the present instructions at the time of operation. Their underlying principle can be summarized as follows: “If condition “X” is achieved, perform action “Y.”
Model-based reflex agents, as the name implies, are best on existing, pre-trained AI models. All decisions and actions the agent takes are based on the fundamental model. The AI model uses algorithms to determine the most fitting condition and appropriate action. An MPC (Model Predictive Control) [7]is one of the best examples of a model-based reflex agent. It uses an internal model of the vehicle’s and environment’s dynamics to control it autonomously.
Goal-based agents are goal-centric in that developers design them to achieve their intended goal exclusively. With goal-based agents, every action and decision shrinks the distance to their goals. They’ll continuously make these decisions and perform actions until they achieve them. For instance, a delivery robot will use sensors to scan its environment and use the data it collects to map its environment until it finds its way to the delivery destination.
Utility-based agents are a lot like goal-based agents. What sets the two apart is that utility-based agents use more parameters and different utilities to find the most efficient means to attain their end-use cases. A self-driving car is a utility-based agent using various utilities like cameras, GPS systems, sensors, and others to find and drive passengers to their destination via the most optimal route.
Learning agents are arguably the smartest of the bunch. These agents continually learn from their input data to better their functionality through a feedback loop. Users give these agents feedback, which the agents use to refine their algorithms and adjust their behavior to reduce errors, give better responses, and make improved decisions.
The most prevalent form of multiagent systems (discussed above) is hierarchical agents. These agents are employed in overly complex environments requiring more than one agent. Here, the agents are arranged in a hierarchy with one high-level agent and two or more low-level ones.
The high-level agents break the task into smaller sub-tasks and assign them to the low-level ones based on the subtasks’ complexity and their agents’ capabilities. The high-level agents also monitor the operations of the low-level agents and ensure they accomplish their required functions.
Contrary to popular thought, agents are not a new concept or technology. Let’s have a look at some AI agents examples. The first AI agent, ELIZA, was developed as early as the 1960s[8]. Over the years, these agents have evolved into sophisticated systems performing complex tasks.
Some common AI agents examples include:
Here are some examples where organizations can use AI agents for business:
Customer service is an integral part of every organization, regardless of the operational industry. A single case of poor customer service can drive clients away, which is why organizations need to set up a strong customer service ecosystem where customers’ issues are addressed promptly.
Here are some of the most common ways organizations can use agents in customer service:
Sales and marketing departments can use agents to:
In recruitment, agents can be used to:
While agents are quite helpful in automating business workflows, there are challenges that organizations need to deal with when deploying autonomous agents.
These include:
Training and deploying agents need a substantial amount of computing resources. If you are going to implement these agents on-premise, you will need to invest in and maintain a resource-intensive infrastructure that’s not easily scalable.
Organizations need huge volumes of data to develop and operate these systems. When dealing with such massive amounts of data, it becomes easy to let sensitive content slip through the net of security and privacy guidelines. It’s therefore important for organizations to have the right tools to manage their data. They should also be aware of and adhere to data privacy requirements and employ the necessary measures to enhance data security.
AI systems trained on biased data produce biased results. For instance, if a hiring algorithm is trained on data from a female-dominated profession, it might favor female candidates over equally qualified male employees. To prevent this from happening, organizations need to train their models on diverse data and regularly test their systems for bias.
As we move into a future where AI plays a very critical role in every industry, we are bound to see more organizations using AI agents for business. This means that they will no longer have to sacrifice quality for cost. Instead, they can use agents to optimize processes, improve decision-making, and enhance customer service all at once.
However, as these applications become more common, questions surrounding privacy, bias, and accountability emerge. As such, organizations must balance the advantages of these applications with the ethical implications they pose in order to fully enjoy the benefits.
References
[1] Conveyco.com. Warehouse automation Statistics. URL: https://www.conveyco.com/blog/warehouse-automation-statistic. Accessed on August 9, 2024
[2]Igi-global. com. Forecasting Demand Using RNN. URL: https://www.igi-global.com/chapter/forecasting-demand-using-recurrent-neural-networks-rnn/345007. Accessed on August 9, 2024
[3] Business.com. The Importance of Scalable Business Models. URL: https://www.business.com/articles/the-importance-of-scalable-business-models. Accessed on August 9, 2024
[4] Superannotate.com. LLM Agents. URL: https://www.superannotate.com/blog/llm-agents. Accessed on August 9, 2024
[5] Sciencedirect.com. Control Theory. URL: https://www.sciencedirect.com/topics/social-sciences/control-theory. Accessed on August 9, 2024
[6] Virtusa.com. Genetic Programming. URL: https://www.virtusa.com/digital-themes/genetic-programming,Accessed on August 9, 2024
[7] Sciencedirect.com,. A Deep Learning Architecture for Predictive Control. URL: https://tiny.pl/dk8bc. Accessed on August 9, 2024
[8] The verge.com. From Eliza to ChatGPT: why people spent 60 years building chatbots. URL:
https://www.theverge.com/24054603/chatbot-chatgpt-eliza-history-ai-assistants-video. Accessed on August 9, 2024
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