AI Agents: what they are and how they will completely transform the way we work

Who hasn’t dreamed of having an assistant to help you with your tasks when you’re having a bad day or when you don’t know how to tackle that new project that’s keeping you awake at night?

And what if it was an assistant who knows your clients well, who offers detailed advice, or shares the information you need the most? Sounds good, doesn’t it? Well, this is what AI agents have been created for. But it doesn’t stop there, as these agents are a powerful tool for companies to scale their teams quickly, achieve key performance indicators, and solve problems before they become a major conflict.

These AI agents are just at the beginning of their full potential, and we’ve compiled their most important features, benefits, examples, and the keys to implementing them successfully. Check it out!

What are AI Agents

An AI agent refers to a system or program that is capable of autonomously performing tasks on behalf of a user or another system by designing its workflow and using available tools.

These agents take the power of generative AI a step further, as they can do a wide variety of things, from answering questions to more complicated or multi-step tasks. In fact, they can act on behalf of the person who customized them, taking care of the most routine needs and thus boosting productivity, saving time and money.

They use LLMs’ advanced natural language processing techniques to understand and respond to user input step-by-step and determine when to turn to external tools. They can be deployed in various applications to solve complex tasks in various business contexts, such as software design, AI automation, code generation tools, or conversational assistants.

How do AI Agents work?

Traditional LLMs generate their responses based on the data used to train them, but they have knowledge and reasoning limitations. AI agent technology, on the other hand, uses backend tool calls to obtain up-to-date information, optimize workflow, and autonomously create subtasks to achieve complex objectives.

In this process, the autonomous agent learns to adapt to user expectations over time. This ability to store past interactions in memory and plan future actions fosters a personalized experience and comprehensive responses.

Furthermore, these tool calls can be achieved without human intervention and expand the possibilities for real-world applications of these AI systems. The three stages that AI agents typically adopt are:

Initialization and planning of goals

Although AI agents are autonomous in their processes, they need human-defined goals and environments. Given the user’s goals and the agent’s available tools, the agent performs a decomposition of tasks to improve performance, as well as a plan of these tasks and subtasks to achieve the complete goal.

If the tasks are simple, planning is not a necessary step. Instead, an AI agent can iteratively reflect on its responses and improve them without planning its next steps.

Reasoning

AI agents base their actions on the information they perceive, as they often do not have the complete knowledge base needed to address all subtasks of a complex objective. To solve this, AI agents use the tools at their disposal, be it external data, web searches, APIs, and even other agents. Once the missing information is retrieved, the agent can update its knowledge base, reevaluate its action plan, and self-correcting.

Learning and reflection

After forming its response, the agent stores the learned information along with user feedback to improve performance and adjust to user preferences for future goals.

Feedback from multiple agents can be especially helpful in minimizing the time users spend providing instructions. However, users can also provide feedback throughout the agent’s actions and internal reasoning to better align results with the intended goal.

These feedback mechanisms help improve the agent’s reasoning and accuracy, known as iterative refinement, and thus avoid repeating the same mistakes.

Agentic AI Issue Resolution

The adoption of AI agents offers a wide range of benefits, as well as transforming the way companies interact with their customers and manage their service operations.

Task automation

With ongoing advances in generative AI, there is a growing interest in workflow optimization through intelligent automation.

AI agents can automate complex tasks that would otherwise require human intervention. This translates into achieving goals economically, quickly, and on a large scale.

In addition, these advances mean that human agents do not need to provide instructions to the AI assistant to create and navigate their tasks.

Increased performance

Multi-agent frameworks tend to perform better than single agents. This is because the more action plans available to the agent, the more learning and reflection will occur.

An AI agent that incorporates knowledge and feedback from other agents specializing in related areas can be useful for information synthesis. This collaboration and ability to fill information gaps are unique to agent frameworks, making them a powerful tool and a significant advancement in the field of AI.

Increased efficiency

AI agents can handle multiple customer interactions simultaneously, significantly reducing response times and increasing the efficiency of customer service operations.

They are also able to identify whether to refer the case to a human and select the one with the best skills to handle the query. This enables companies to handle higher volumes of queries without compromising service quality.

Increased customer satisfaction

AI agents provide fast and accurate responses, leading to improved customer service scores.

They can use data to personalize interactions, improving the overall outcome, and learning over time, resulting in continuous improvement.

Availability 24/7

AI agents are available 24 hours a day, 7 days a week. This ensures that customer inquiries are handled faster, regardless of time zones or business hours.

This helps companies meet customer expectations and improve customer loyalty.

Scalability

These agents can be easily scaled to handle higher volumes of customer interactions, making them ideal for companies looking to grow without compromising the quality of service.

As case volume increases, agents can be easily adjusted to handle the additional load. This ensures consistent and reliable support.

Consistency and accuracy

AI agents provide consistent and accurate responses to customer queries, reducing the risk of errors and ensuring that customers receive reliable information.

They can improve the accuracy of their responses through agent loops and human-like reasoning. This consistency helps build brand trust as customers receive the best experience.

Types of AI Agents

AI agents can be developed to have different levels of capabilities, depending on the more complex or simpler actions we want to perform:

1. Simple reflex agents: are the simplest form of agent, which base their actions on current perception. It has no memory and does not interact with other agents. This agent is preprogrammed to perform actions that correspond to the fulfillment of certain conditions. If it is faced with a situation for which it is not prepared, it will not be able to respond appropriately. An example would be a thermostat that activates the heating at a certain time every day.
2. Model-based reflex agents use their current perception, such as their memory, to maintain an internal model of the world. As the agent continues to receive new information, the model is updated. They can store information and can operate in partially observable and changing environments, although they are limited by their set of rules.
3. Goal-based agents: have an internal model and also a goal or set of goals. These agents search for sequences of actions that achieve their goals and plan these actions before acting accordingly. This search and planning improves their effectiveness compared to the first two types.
4. Utility-based agents: select the sequence of actions that will achieve the objective and also maximize the utility or reward. It uses a utility function, which assigns a value, a metric that measures the utility of an action that the agent will do, or to each scenario based on a set of fixed criteria. The agent selects the actions that maximize the expected utility, so they are useful in cases where multiple scenarios achieve a desired goal.
5. Learning agents: have the same capabilities as above, but are unique in their ability to learn. They add new experiences to their initial knowledge base, producing it autonomously. This learning improves their ability to operate in unfamiliar environments.

AI Agents Examples

Companies in different industries that are incorporating AI agents into their processes are already seeing the great benefits that AI can bring. This technology is very versatile and can create use cases for different industries and tasks:

Financial industry

From unified customer data, an AI agent can extract relevant information for its workers, tailoring financial recommendations to each customer’s needs and objectives.

In addition, these agents can help prepare for customer meetings through tasks such as accurately summarizing customer support interactions and avoiding human error. They can automatically summarize open cases or orders, invoices, and recent activity, saving a lot of time and money.

Manufacturing

Agents can monitor machinery to predict maintenance requirements and optimize production processes. This increases productivity and helps reduce costly downtime.

They are also very useful for sales teams, as they can assist them in the different transactions throughout the process. They can summarize agreements to highlight deviations in planned versus actual quantities and revenues, helping to make better, informed decisions.

Healthcare

AI agents can deliver high-level patient experiences. They not only answer questions but also help patients schedule the best medical service for their needs.

An AI agent can review coverage benefits, generate medical history summaries, and approve requests for care. They can also create customized treatment plans and assist with records management.

Productivity tools

We found concrete AI agent tools like GitHub Copilot, which helps software developers through code suggestions.

They would be the equivalent of having a second set of eyes that are always available to help. By offering real-time suggestions, the agents improve productivity and save a lot of time.

Email management

AI Agents can also help manage and administer the inbox. They can sort emails, flag important ones, and even provide intelligent responses to save time.

They have features such as intelligent wording, like Google’s Gmail, which helps users respond to emails faster by suggesting phrases based on context.

AI Agentic Workflows

Despite their potential, AI agents pose certain risks around technical limitations, ethical concerns, and broader societal impacts associated with a system’s level of autonomy.

Technical risks include bugs and malfunctions, as well as security concerns, including the possibility of automating cyberattacks. The autonomous nature of AI agents raises ethical questions about decision-making and accountability.

Leveraging the benefits of AI agents while mitigating risks will depend on the context of the specific agent environment and its application. Some of the measures that organizations should consider include:

• Improve agent transparency and implement “human” oversight, allowing agents to work autonomously while human experts review decisions.
• Establishing clear ethical guidelines that prioritize human rights, privacy and accountability is an essential step to ensure that AI agents make decisions aligned with human and societal values.
• Prioritize data governance and cybersecurity before deploying AI agents.
• Implementing public education and awareness strategies to mitigate the risks of over-reliance and disempowerment in social interactions with AI agents.

The rise of AI agents is not just a technological shift, but a transformation in the way we contextualize work and human-machine collaboration. At Plain Concepts, we have years of experience in the field of artificial intelligence and can help you understand the capabilities and limitations of AI agents, as well as implement a well-thought-out strategy. We will ensure that you can position yourself to take advantage of the full potential of this technology that will transform everything while ensuring that the associated risks are mitigated.

Maintaining the balance between adopting innovative technologies and ensuring responsible implementation will be critical to thriving in this new landscape. Contact us and gain access to a business landscape you never expected!