AI (or agricultural) agents are one of the MAIN TECHNOLOGICAL TRENDS OF 2025 and among those that grow the most in the business sector. A revolution within the revolution of the era of artificial intelligence that invades us, with an impressive income generation potential and at the same time obstacles to its effective implementation that are also large.
A recent report from the Capgemini Research Institute puts us on the track of the current situation. Entitled “Age of agricultural AI: how trust is key to collaboration between humans and Ia”reveals information obtained from a survey of 1,500 senior executives from 14 countries on the emergence of AI agents as a transformative force in companies.
The report calculates an impressive potential for this technology, since they could generate until 450,000 million dollars in economic value through income growth and cost savings in the markets surveyed. But not everything is positive and the same report indicates that Trust in totally autonomous agents is decreasing And very few companies report sufficient level of maturity in terms of data infrastructure and technology necessary to implement AI with agents.
As ia agents represent The next border in artificial intelligenceif a company wants to stay at the forefront, it is a good time to explore how they can take advantage of promoting growth and business innovation. We review a phenomenon that aims to revolutionize the technology industry.
What are AI agents?
The boom of the generative AI has given rise to the appearance of new technologies that expand the limits of artificial intelligence assistance as we know it. One of the most recent and most interesting developments is agricultural AI. Depending on the cases of use, the supply of products and the company, you may see various definitions of AI agent. However, in general, we can define them as A software tool that can perform tasks autonomously.
Capgemini’s definition is more complete: «AI agents are programs/platforms/software connected to the business environment with a defined limit, make decisions autonomously and act to achieve specific objectives with or without human intervention. With the latest advances in AI reasoning models, AI agents can break down tasks, analyze possible ways to find solutions to the problem posed, try them and present satisfactory results ».
To perform the task, they can use a combination of instructions provided by humans and environmental triggers, as well as, and this is perhaps the most important element, Your own reasoning capacity To deduce what should be done and when. IA agents are those who delegate tasks and must have intelligence to think about what is needed to do it. They can determine what tools are needed and in those cases communicate with humans to request them.
These AI agents differ from other AI technologies that we use daily (such as chatbots or AI attendees), because They can execute tasks in your name and identify when necessary, instead of simply providing an answer from a predetermined subset of information. Therefore, the potential for assistance that can provide individuals and companies is significantly higher.
How does an AI agent work?
An AI agent is based on technology with which you are probably familiar, combining the latest capabilities of AI in a single package to take care one step further. For example, take advantage of tools such as extensive language models (LLM), reasoning, processing of natural language, extensive context windows and others To perform tasks in an intelligent and adaptive way.
Agents are a technological capacity that uses the possibilities of AI, such as automatic learning and natural language processing, to understand the context of an interaction, a client’s need or a commercial transaction, and then apply automation to manage the workflow associated with that context.
To perform tasks, an AI agent is based on a combination of human management and data or context to which you have access permission. It can also be designed to react to external triggers, such as receiving an email, which can initiate a chain of actions or decisions. Ultimately, the information and the context it uses depend on the case of specific use of the user, which can vary from simple to very complex, according to the needs of the company.
What makes them different from chatbots
A chatbot of AI can provide the user with the solution to a question by consulting their training data. However, you can only perform the tasks assigned to you. An AI agent can use the context of the information around him to extract his own conclusions and perform the task, which requires a higher level of reasoning. While a chatbot is rather a dialogue system with which you can talk, the agents of AI go much further: do things for you.
A real example would be to make an order of groceries. If you use a chatbot or an IA assistant to ask what to ask for dinner, this can generate a list and, in more advanced cases, even order. However, I would not have the analysis capacity to incorporate other contexts, such as checking what you already have in the refrigerator (if you have an intelligent house inventory), analyze your dietary preferences in your health application, coordinate with your calendar to know when you will be at home to receive the order, and a length etc.
Business use cases
The decision to implement AI agents in the workflow can be overwhelming, given the large number of options available for various business functions. However, a good starting point is as simple as Identify which company processes may need additional assistance.
And it is that business use cases for AI agents can be as simple or complex as desired. While some companies offer AI agents that can perform autonomous tasks in their equipment, such as purchases, sales, customer interactions and more, a company can use AI agents to expedite a task as simple as sending a message.
These agents applications do not have to be too elaborate. In fact, instead of trying to implement complex agents that address multiple steps challenges, it is better carefully analyze your own business processes and choose those who have a greater impact, even if they are simpler.
What should be evaluated when considering the implementation of AI agents is the ‘Hygiene’ of a company data infrastructure. This advice is not new, since it is also a fundamental component in most of the digital transformation opportunities; However, it is an aspect that is often overlooked in business workflows.
Risks and challenges to overcome
Since AI agents can perform tasks for you, there is a great concern about the possibility that They become intrusive and wreaked havoc on workflows personal or business. However, the first thing you should remember is that they are trained to meet specific needs and cannot work outside those limitations.
IA agents can only do what human allow them, so there is a set of actions they can perform and a set of permits that have within those actions. This does not mean that AI agents are infallible. In short, they are still AI models and, as seen with simpler tools such as chatgpt, they often hallucinate or issue false information.
Artificial intelligence is intelligence, yes, But it is still artificialand they are still necessary to establish paths to travel and limits around them, experts point out. Appropriate security measures can prevent these problems from affecting a business. Specific precautions can be taken during and after the training process to guarantee the best results. When developing these agents, it is important to have evaluation sets, data sets, quality analysis and alarms to monitor when the agent does not work as expected for use cases.
There is a way to go. Companies are developing AI agents to automate tasks, improve efficiency and offer personalized experiences to their customers. However, the implementation and management of these intelligent agents can be a huge challenge. Gartner’s last report on agricultural AI spoke of ‘pure hype’ and assured that most of the AI projects with agents continued experiments in initial stages or demonstrations of proof of concept, more driven by advertising than by practical results.
Gartner predicted that more than 40% of the so -called agency projects would be canceled at the end of 2027. According to the consultant, this technology is too expensive, it has an uncertain commercial value and, often, does not adapt well to real cases of use, which often harms organizations instead of helping them. The forecast coincides with Capgemini’s report when he talks about The maturity level is still very low: Only 2 % of organizations have implemented large -scale AI agents, 12 % at partial scale, 23 % have launched pilots and 61 % are still exploring the implementation.
And trust down. Trust in totally autonomous AI agents is decreasing, according to the report and has dropped from 43 % to 27 % in one year. Ethical concerns, lack of transparency and limited understanding of agents are key barriers. Ethical concerns about AI, such as data privacy, algorithmic bias and the “AI black box” are frequent, but few organizations act with decision to mitigate them.
The collaboration between humans and the future is. The mixed teams, where human and agents of AI collaborate, will become the norm, promoting productivity and innovation. This means that organizations expect to have AI agents as members of human supervised teams, where these agents must be seen not as tools, but as part of the team. But much remains to be done.
* Images generated by AI
