What are AI agents?

A software or system that can independently carry out activities on behalf of a user or another system by planning its workflow and using the resources at its disposal is known as an artificial intelligence (AI) agent. Beyond natural language processing, AI agents may perform a wide range of tasks, such as making decisions, solving problems, interacting with the outside world, and carrying out activities.

From software design and IT automation to code-generation tools and conversational assistants, these agents can be used in various applications to accomplish complicated tasks in various organizational scenarios. Large language models (LLMs) employ sophisticated natural language processing techniques to understand and react to user inputs step-by-step and decide when to use external tools.

The operation of AI agents

Large language models (LLMs) are the foundation of AI agents. Because of this, AI agents are frequently called LLM agents. Conventional LLMs, like IBM® GraniteTM models, are constrained by knowledge and reasoning constraints and generate their answers based on the data used to train them. On the other hand, agentic technology makes use of tool calling on the backend to get the most recent information, streamline processes, and independently generate subtasks to accomplish complicated objectives.

Over time, the autonomous agent can adjust to user expectations. A customized experience and thorough responses are promoted by the agent's capacity to remember previous exchanges and anticipate future actions.

This tool expands the potential uses of these AI systems in the real world and can be accomplished without human intervention. These three steps make up the strategy AI agents employ to accomplish user-specified goals:

Setting and organizing goals

Even if AI agents make decisions independently, they still need human-defined objectives and settings. Three primary factors affect autonomous agent behaviour:

• The group of programmers creates and instructs the agentic AI system
• The group that makes the agent available to the user and deploys it

• The user specifies the available tools and gives the AI agent particular tasks

The AI agent then performs task decomposition to enhance performance based on the user's objectives and the tools. The agent devises a strategy comprising distinct tasks and subtasks to achieve the intricate objective.

Planning is not a crucial step for leisurely activities. Rather than planning its next move, an agent might iteratively evaluate and refine its replies.

Using the tools at hand to reason

The information that AI agents see informs their actions. AI agents frequently lack the comprehensive knowledge base required to handle every subtask inside a complex goal. AI agents employ the resources at their disposal to address this. Web searches, APIs, external data sets, and even other agents can be used as these tools. The agent can update its knowledge base via these tools once the missing data has been recovered. This implies that the agent self-corrects and reevaluates its strategy at every stage.

Consider a user organizing their trip to understand this procedure better. The user tasks an AI agent to forecast which week of the following year would have the finest weather for their Greek surfing trip. The agent collects data from an external database containing daily weather reports for Greece over the previous few years because the LLM model is not an expert in weather patterns.

The next subtask is formed since, even with this new information, the agent can still not identify the best weather conditions for surfing. The agent interacts with an outside agent specializing in surfing for this subtask. Suppose that while doing so, the agent discovers that the ideal surfing circumstances are high tides and sunny weather with minimal to no rain. Now, the agent may find patterns by combining the knowledge it has gained from its tools. It may forecast which week in Greece will have high tides, sunny skies, and a slight probability of rain the following year. The user is then shown these results. AI agents are more versatile than conventional AI models because of this information exchange between tools.

Education and introspection

AI agents use feedback mechanisms like human-in-the-loop (HITL) and other AI agents to increase the precision of their reactions. To illustrate this, let's go back to our earlier surfing scenario. Following the formulation of its response to the user, the agent retains the knowledge it has gained and the user's input to enhance performance and adapt to the user's preferences for upcoming objectives.

Feedback from other agents may also be used if they were employed to achieve the aim. The time that human users spend giving instructions can be reduced with the help of multi-agent feedback. Users can also offer feedback while the agent performs its actions and internal logic to better match the outcomes with the desired result.

Feedback mechanisms enhance the logic and accuracy of the AI agent, a process known as iterative refinement. AI agents can also retain information about solving past problems in a knowledge base to prevent making the same mistakes.

AI Agent vs AI Chatbot: Key Differences

Below is a comparison table highlighting the distinctions between AI Agents and AI Chatbots:

Feature	AI Chatbots	AI Agents
Primary Purpose	Interacts with humans, primarily for customer support or answering questions	Executes automated and independent tasks, often without human interaction
Automation Capability	Lacks full automation capability, relying on human interaction	Fully automates tasks without human intervention
Human Interaction	Mainly interacts with humans via text or voice	May not require human interaction during operation
Form	Primarily exists as text or voice-based interfaces (chat applications, chatbots, or virtual assistants)	Can be software, physical robots, or smart home devices (e.g., vacuum robots, smart thermostats)
Task Processing Ability	Limited to basic questions and answers, with less capability for handling complex tasks	Capable of handling complex tasks like automation and data-driven decision-making
Application Scope	Primarily in customer support, answering queries, and chat-based interactions	Broad, applicable in various domains such as automation, medical diagnostics, and personal finance
Response Mechanism	Reacts based on predefined scripts and dialogue models, often limited in contextual understanding	Proactive and reactive to the environment, capable of planning and complex decision-making
Learning Capability	Typically lacks continuous learning ability; updates are manual	Continuously learns and adapts based on feedback from the environment and other agents
Context Processing	Relies on fixed scripts, struggles with non-standard requests	Deep understanding of context and emotions, capable of processing social signals and complex scenarios

Paradigms of reasoning

The architecture used to create AI bots is not standardized. Multi-step problems can be solved using a variety of paradigms.

Reasoning and Action, or ReAct

This paradigm allows us to teach agents to "think" and plan after every action and tool response to determine which tool to use next. These Think-Act-Observe cycles are used to improve replies and solve challenges step-by-step iteratively.

Agents can be taught to reason slowly and exhibit each "thought" using the prompt structure. The verbal reasoning of the agent provides information on the formulation of responses. According to this concept, agents constantly add fresh reasoning to their context. One way to think of this is as a type of Chain-of-Thought prompting.

Reasoning Without Observation, or ReWOO

In contrast to ReAct, the ReWOO approach does not rely on tool outputs for action planning. Agents instead make plans in advance. You can avoid using the same tools twice by predicting the tools to use when the user first prompts you. From a human-centred standpoint, this is preferable since the user may verify the plan before it is carried out.

Three elements comprise the ReWOO workflow. When a user prompts the agent in the planning module, it anticipates what it will do next. The next step is to collect the outputs generated by executing these tools. Finally, the agent creates a response by combining the tool outputs and the original strategy. Token usage, processing complexity, and the consequences of intermediary tool failure can all be significantly decreased by this proactive preparation.

AI agents types

It is possible to create AI bots with different skill levels. A simple agent could be selected for simple aims to reduce needless processing complexity. There are five primary types of agents, ranked from most basic to most sophisticated:

1. Simple reflex agents

A simple reflex agent is the most basic agent that bases behaviour on present perception. This agent has no memory; if it lacks information, it doesn't communicate with other agents. These agents work according to a set of rules or reflexes. This indicates that the agent has been preprogrammed to function in a way consistent with fulfilling specific requirements. The agent cannot react effectively if it comes upon a scenario for which it is unprepared. The agents can function effectively only in fully observable situations with access to all relevant data.

An example would be a thermostat that activates the heating system each night at a predetermined time. The condition-action rule in this case is that the heating is turned on, for example, at 8 PM.

2. Model-based reflex agents

Model-based reflex agents keep an internal model of the world by using both their memory and present perception. The model is modified as the agent continues to learn new knowledge. The model, reflexes, past precepts, and present state influence the agent's behaviour.

Unlike simple reflex agents, these agents can function in partially visible and dynamic contexts and retain memory information. They are still constrained by their regulations, though.

A robot vacuum cleaner, for instance. It detects obstructions, such as furniture, and manoeuvres around them while cleaning a filthy space. To avoid being trapped in a cleaning cycle, the robot also keeps a model of the regions it has already cleaned.

3. Agents with a goal

In addition to having a goal or set of goals, goal-based agents also have an internal model of the world. These agents plan their actions before taking them, and they look for action sequences that accomplish their objectives. Compared to essential and model-based reflex agents, their efficacy is increased by this search and planning.

An illustration would be a navigation system that suggests the quickest path to your destination. The model considers several ways to get to your goal or destination. In this case, the agent's condition-action rule dictates that it will suggest a faster path if one is discovered.

4. Utility-based agents

Utility-based agents choose the course of action that maximizes utility or reward while achieving the goal. A utility function is used to calculate utility. This function gives each scenario a utility value based on a set of predetermined criteria. This metric quantifies the usefulness of an action or how "happy" it will make the agent.

The criterion may include the goal's progress, the time needed, or the computational complexity. The agent subsequently chooses the acts that maximize the expected utility. These agents are, therefore, helpful when there are several ways to accomplish a goal, and it is necessary to choose the best one.

For instance, a navigation system that suggests the best fuel-efficient route to your destination while reducing traffic time and toll expenses. This agent uses this set of criteria to quantify utility and choose the best path.

5. Learning agents

While learning agents are distinct from other agent kinds, they possess the same characteristics. Their original knowledge base is expanded by new experiences that happen independently. This learning improves the ability of the agent to function in new environments. Four primary components make up learning agents, which can have utility or goal-based reasoning:

• Learning: The agent's knowledge is enhanced by using its sensors and precepts to learn from the surroundings.

• Critic: This tells the agent if the calibre of its answers satisfies the performance requirement.

• Performance: This component is in charge of choosing what to do after learning.

• Problem generator: This generates different suggestions for what should be done.

For instance, tailored suggestions for online stores. These agents keep note of user choices and activities in their memory. Based on this information, the user is given recommendations for particular goods and services. Every time new suggestions are offered, the cycle is repeated. The user's activities are continuously recorded for educational purposes. By doing this, the agent gradually increases its accuracy.

AI agent use cases

Experience of the customer

By acting as virtual assistants, offering mental health support, mimicking interviews, and performing other relevant functions, AI agents can be included in websites and applications to improve the user experience. Developing the abundance of no-code templates for user installation further simplifies developing these AI agents.

Healthcare

Numerous real-world healthcare applications are possible using AI agents. In these situations, multi-agent systems can be beneficial for solving problems. These technologies free up medical personnel's time and energy for other pressing duties, such as managing medication processes and treatment planning for patients in the emergency room.

Emergency action

AI agents can use deep learning techniques to retrieve user data from social media platforms during a natural disaster. These users' locations can be mapped to help rescue agencies save more lives faster. Thus, AI agents have the potential to significantly improve human lives in both routine chores and life-saving scenarios.

Benefits of AI agents

Automation of tasks

Workflow optimization with AI, or intelligent automation, is becoming increasingly popular as generative AI progresses. AI bots can automate complex tasks that would typically require human resources. This means that objectives can be accomplished quickly, cheaply, and in large quantities. Consequently, these developments eliminate the need for human agents to guide the AI helper in developing and completing its tasks.

Improved performance

Singular agents typically perform worse than multi-agent frameworks. This is because learning and reflection happen more when an agent has more options for proceeding. Information synthesis may benefit from an AI agent integrating insights and comments from other AI agents with similar specializations. Agentic frameworks are a potent tool and a significant development in artificial intelligence because of their capacity to bridge knowledge gaps and collaborate with AI agents on the backend.

Response quality

Compared to standard AI models, AI agents offer more thorough, precise, and tailored responses to the user. Since better responses generally result in a better customer experience, this is essential to us as users. As previously explained, this is accomplished by updating their memory stream, utilizing external tools, and communicating information with other agents. These habits are not preprogrammed; instead, they develop naturally

Risks and limitations

Dependencies between multiple agents

Multiple AI agents must be knowledgeable about complex jobs. There is a chance of malfunction while putting these multi-agent frameworks into practice. It's possible for multi-agent systems constructed using the same foundation models to encounter common problems. Such flaws could lead to a failure of all agents involved in the system or expose the system to harmful attacks. This emphasizes that data governance is essential to developing foundation models and rigorous training and testing procedures.

Never-ending feedback loops

Concerns are associated with the ease with which human users use AI bots for hands-off reasoning. Agents may use the same tools repeatedly, creating endless feedback loops if they cannot develop a thorough plan or think critically about their findings. A certain amount of real-time human monitoring may be employed to prevent these redundancies.

Complexity of computation

In addition to being time-consuming, creating AI agents from scratch can be highly computationally costly. It can take a lot of resources to train a high-performance agent. Depending on how complicated they are, agents may also take several days to finish jobs

The best methods

Activity records

Developers can give users access to a log of agent actions to solve the issues of multi-agent dependencies. These actions can involve using external tools and describing the external agents to accomplish the aim. This openness fosters trust, gives users visibility into the iterative decision-making process, and offers the chance to identify mistakes.

Interrupting

It's advised to keep AI agents from operating for extended periods of time. Especially when it comes to malfunctioning because of design flaws, changes in access to specific tools, or unintentional infinite feedback loops. Using interruptibility is one method to achieve this. Allowing human users to halt a series of activities or the entire operation gracefully is necessary to keep control of this. It takes careful consideration to decide whether and when to interrupt an AI agent because some terminations can have more negative effects than positive ones. In a life-threatening situation, for example, letting a malfunctioning agent continue to help rather than shutting it down entirely can be safer.

Distinct agent identifiers

Unique identities can be used to reduce the possibility that agentic systems will be misused. It would be easier to trace the origin of the agent's developers, deployers, and users if these identities were necessary for agents to access external systems. This would be especially useful if the agent were to utilize it maliciously or cause unintentional harm. The operating environment for these AI agents would be safer with this degree of responsibility.

Human oversight

Giving AI agents human comments occasionally can help them learn, especially when they are first introduced to a new environment. This enables the AI agent to evaluate its performance against the predetermined benchmark and make necessary adjustments. This input type enhances the agent's ability to adjust to user preferences.

Aside from this, it is excellent practice to wait for human clearance before an AI agent performs activities that have a significant impact. For example, human confirmation should be required for everything from financial trading to sending bulk emails. For such high-risk domains, some degree of human monitoring is advised.

In summary, AI Agents are not just a technological trend but a powerful automation solution for modern businesses. With autonomy, AI Agents can operate independently, make decisions based on data and real-world environments, and help enterprises optimize their processes.

If you are looking for a comprehensive AI solution, HILIOS AI is your trusted partner to unlock the full potential of AI. Visit the HILIOS AI website or contact us via hotline or email for detailed consultations and to explore the most suitable solutions for your business.