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!

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.

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.

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.

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.

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.

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!