AI Agents: Overview and Application in AInexxo

AI agents refer to systems or programs that are capable of autonomously performing tasks on behalf of users or other systems by designing their workflows and utilizing available tools.

AI agents can encompass a wide range of functionalities beyond natural language processing, including decision-making, problem-solving, interacting with external environments, and executing actions.

These agents can be deployed in various applications to solve complex tasks in diverse enterprise contexts, from software design and IT automation to code-generation tools and conversations.al assistants. They use the advanced natural language processing techniques LLMs to comprehend and respond to user inputs step-by-step and determine when to call on external tools. 

Agentic System Architecture

The overall framework of AI agents consists of three key parts: brain, perception, and action. 

  • Brain: The brain is mainly composed of a large language model, which not only stores knowledge and memory, but also undertakes information processing and decision-making functions, and can present reasoning and planning processes to deal well with unknown tasks. 
  • Perception: The core purpose of the perception module is to expand the perceptual space of the agent from the pure text domain to include textual, auditory and visual modalities. 
  • Action: In the construction of the agent, the action module receives the action sequence sent by the brain module and performs actions that interact with the environment. 

How does an AI agent work? 

At the core of AI agents are Large Language Models (LLMs). For this reason, AI agents are often referred to as LLM agents. Traditional LLMs, produce their responses based on the data used to train them and are bounded by knowledge and reasoning limitations. In contrast, agentic technology uses tool calling on the backend to obtain up-to-date information, optimize workflow and create subtasks autonomously to achieve complex goals. 

In this process, the autonomous agent learns to adapt to user expectations over time. The agent’s ability to store past interactions in memory and plan future actions encourages a personalized experience and comprehensive responses. This tool calling can be achieved without human intervention and broadens the possibilities for real-world applications of these AI systems. 

The approach that AI agents take in achieving goals set by users is comprised of these three stages: 

  • Determine goals

The AI agent receives a specific instruction or goal from the user. It uses the goal to plan tasks that make the final outcome relevant and useful to the user. Then, the agent breaks down the goal into several smaller actionable tasks. To achieve the goal, the agent performs those tasks based on specific orders or conditions.  

  • Acquire information 

AI agents need information to act on tasks they have planned successfully. For example, the agent must extract conversation logs to analyze customer sentiments. As such, AI agents might access the internet to search for and retrieve the information they need. In some applications, an intelligent agent can interact with other agents or machine learning models to access or exchange information.  

  • Implement tasks 

With sufficient data, the AI agent methodically implements the task at hand. Once it accomplishes a task, the agent removes it from the list and proceeds to the next one. In between task completions, the agent evaluates if it has achieved the designated goal by seeking external feedback and inspecting its own logs. During this process, the agent might create and act on more tasks to reach the final outcome.  


What are the types of AI agents? 

  • Single agent applications: Agents can serve as personal assistants to help users get rid of daily chores and repetitive labor. They are able to independently analyze, strategize, and solve problems, reducing individual workload and improving task solving efficiency. 
  • Multi-agent systems: Agents can interact with each other in collaborative or competitive ways. This allows them to make progress through teamwork or adversarial interactions. In these systems, agents can collaboratively complete complex tasks or compete with each other to improve their performance. 
  • Human-machine cooperation: Agents can interact with humans, providing assistance and executing tasks more efficiently and safely. They can understand human intent and adjust their behavior to provide better service. Human feedback can also help agents improve their performance. 
  • Professional domains: Agents can be trained and specialized for specific domains such as software development, scientific research, or other industry-specific tasks. They can leverage the pre-training on large corpuses and the ability to generalize to new tasks to provide expertise and support in these areas. 

Benefits of AI Agents

  • Improved productivity 

AI agents are autonomous intelligent systems performing specific tasks without human intervention. Organizations use AI agents to achieve specific goals and more efficient business outcomes. Business teams are more productive when they delegate repetitive tasks to AI agents. This way, they can divert their attention to mission-critical or creative activities, adding more value to their organization. 

Reduced costs 

Businesses can use intelligent agents to reduce unnecessary costs arising from process inefficiencies, human errors, and manual processes. You can confidently perform complex tasks because autonomous agents follow a consistent model that adapts to changing environments.  

Informed decision-making

Advanced intelligent agents use machine learning (ML) to gather and process massive amounts of real-time data. This allows business managers to make better predictions at pace when strategizing their next move. For example, you can use AI agents to analyze product demands in different market segments when running an ad campaign.  

Improved customer experience 

Customers seek engaging and personalized experiences when interacting with businesses. Integrating AI agents allows businesses to personalize product recommendations, provide prompt responses, and innovate to improve customer engagement, conversion, and loyalty.  


Risks of AI agents

Loss of Generality 

When an agent-based architecture focuses on highly specialized models, there is a risk that the model loses its generality. While a broad range of capabilities may be beneficial for specific tasks, it often requires significant effort and a larger workforce to maintain and develop these specialized models. 

Monitoring and Observation Challenges 

Monitoring and observing the system becomes more difficult when processes are elongated or more complex. As agents handle a wider variety of tasks and operations, it can take longer to execute and assess their performance. This extended duration complicates the ability to determine, in advance, what constitutes good system behavior. Consequently, it becomes challenging to implement effective oversight and make real-time adjustments, increasing the risk of unnoticed errors or inefficiencies. Establishing robust monitoring mechanisms is essential, but these can add further complexity and resource demands to the overall system management. 

Single Model Vulnerability  

An agent-based architecture that relies on a single model poses a significant risk: the vulnerabilities of that model can extend throughout the entire system. If the underlying model has limitations—such as biases in training data, an inability to handle certain inputs, or a tendency toward hallucinations—these flaws can propagate across all agents utilizing it. This leads to a decrease in the overall robustness of the system, with the potential for errors or inefficiencies to recur in multiple areas of the infrastructure. To mitigate this risk, it is essential to adopt a multi-model or modular approach that diversifies knowledge sources and enhances system resilience. 

Computational Complexity 

Creating AI agents from scratch is time-consuming and often computationally expensive. Training a high-performance agent requires significant resources. Depending on the complexity of the task, agents can take a long time to complete their operations. To address this issue, it is essential to provide clear and concise instructions through good prompt engineering, which explains in detail the structure and needs of the company, such as the database structure, the nature of the product and the appearance of the documents. This way, the agent will be able to accurately perform the required tasks. 

Code optimization

Parallelization of processes and tools allows for faster execution of operations. Instead of relying exclusively on the internal knowledge of the agent, the use of RAG (Retrieval-Augmented Generation) and external tools offers a more dynamic and efficient approach. 


AInexxo Copilot

AInexxo is a leading provider of hyper-automated virtual assistants, or copilots, designed to communicate with operators, technicians, and engineers in natural language. Specializing in the process and automation industries, our intelligent system is capable of extracting, structuring, and interpreting complex product and process data.

This enables users to query information, troubleshoot issues, and enhance R&D efforts in product development and design. In essence, AInexxo’s copilot serves as a virtual engineer, combining cutting-edge AI models with deep industry expertise to optimize performance and innovation in industrial environments.

Multi-agent and domain-specific

Thanks to our multi-agent and domain-specific approach, our agents adapt perfectly to the business context, improving specialization and ensuring optimal results. Each agent is customized to handle a specific domain and, when necessary, multiple agents work together synergistically to solve complex problems. 

By not relying solely on AI-based tools, agents can utilize a diverse set of tools, equipping them with precise, fast, and less computationally expensive options. This approach reduces the risk of errors or inaccuracies, enhancing operational efficiency and ensuring reliable results.

For the most complex tasks, we employ advanced models, which are well-suited for processing large datasets or solving intricate problems. For simpler tasks, we use more lightweight models, optimizing resource consumption. This strategy achieves an optimal balance between performance and execution speed, offering dynamic and flexible solutions tailored to the specific task.

At AInexxo, we combine LLMs, Computer Vision, Statistics, RAG and other methods to ensure that our agents can not only process natural language but also analyze images, access real-time data, and make well-informed decisions. Moreover, we use a multi-model strategy, selecting from a variety of open-source models of different sizes. This approach makes us not only future-proof but also ensures that we can adapt the most suitable model for any given task. 

Open-Source

Our commitment to open-source technologies is a crucial part of our vision. It allows us to avoid dependency on large corporate providers, giving us the flexibility to choose the best solutions available while staying at the forefront of innovation. By integrating all these elements, we provide highly precise copilots for industrial environments, minimizing errors and enhancing operational efficiency—an essential advantage in industries where precision is paramount. 

Conclusion

With the integration of all these elements, we deliver a highly precise copilot tailored for industrial, automation, and other environments, where minimizing errors and maximizing operational efficiency are crucial. Our approach allows us to provide solutions that consistently meet the high standards demanded by industries where precision is non-negotiable. 

These innovative resources and methodologies position AInexxo at the forefront of the market. By leveraging advanced AI, multi-agent systems, and open-source technologies, we offer cutting-edge, dynamic solutions that not only address today’s challenges but also anticipate future needs. This strategic approach ensures that our customers are always equipped with the best resources to stay ahead of the competition, driving success and innovation in their industries.