In this post, we explore the potential of the AI agent industrial revolution, including key trends and technical considerations for building tools that enable businesses to embrace the transformative potential of these intelligent systems.
AI agents are everywhere. While they were once an option, they have now become a necessity for businesses that want to stay competitive. As 2025 approaches, AI agents are revolutionizing industries by automating tasks and changing the way businesses interact with employees and customers. This post invites you to explore this fascinating landscape.
So, what is an AI agent? An AI agent is a software program that can act autonomously to understand, plan, and execute tasks. AI agents are based on LLMs and can interact with tools, models, and other aspects of a system or network to meet user objectives. According to Statista, the global AI market is expected to reach $1.8 trillion by 2030, and nearly 90% of leaders believe that AI will help their companies increase revenue.
How can we improve our business with the help of AI agents?
How are assistants different from AI agents? The main difference between an AI assistant, which is usually a chatbot, and an AI agent is their level of autonomy and ability to make decisions. AI assistants usually require instructions or input from users and focus on providing real-time information or support. AI agents, on the other hand, can operate more independently. They gather information, process it, and act on predefined objectives.
For example, it’s early Friday morning, and a logistics manager wants to answer the highly ambiguous question of how business is going today and what to watch out for. As the manager interacts with the AI, the system provides more specific answers. You can customize this tool with a company database so that it’s not just ChatGPT, but uses RAG techniques to improve generative AI quality by allowing large language models (LLMs) to leverage additional data resources without retraining.
On Friday, an industrial logistics manager asked his trusted AI agent for advice. He asked the agent to recommend ways to manage product inventory and parcel shipments on different routes. In the age of AI agents, these types of questions can be transformed into an SQL query that breaks down ambiguous requirements into a concrete software system aligned with business needs.
Expert Knowledge: From Human Analyst to Artificial Agent
One current problem is that the expert knowledge systems analysts and business experts used to have when building these big dashboards is now being held by AI agents.
Coinciding with the explosion of big data, many CEOs have complained in recent years that these dashboards are overwhelming and don’t help them make decisions. They need simpler, more concrete answers.
Key AI Trends for 2025
The market for AI agents is growing, but data privacy, bias, and ethics remain major concerns. One thing is certain, though: AI isn’t going away. In the meantime, it’s crucial to understand the challenges that will profoundly impact businesses.
1. Levels of adoption and specialization:
AI agents are evolving beyond basic chatbots and being integrated into various sectors and applications. According to industry reports, AI agents have increased productivity by up to:
– 55% in human resources
– 60% in software development
– 90% in customer service
The trend is shifting towards highly specialized AI agents. A notable example is the oil and gas sector, which was previously discussed in posts featuring 7Puentes, such as «Chat with Your Data.»
2. Autonomy and Proactivity:
«AI agentic» is a major trend in which AI agents become more autonomous and able to plan, execute tasks, make decisions, and learn independently with less human supervision.
3. Multimodal Capabilities and Natural Interaction:
Agents are evolving to understand and process various types of data (voice, images, and video) with seamless multimodality. In this regard, natural language processing (NLP) has advanced to the point where agents can understand complex conversations, context, and even emotional nuances. This results in interactions that are increasingly human-like.
4. Advanced Reasoning for Problem Solving
AI agents are gaining the ability to perform multi-step reasoning by breaking down complex tasks into smaller, more manageable steps in order to find solutions. This includes integration with external tools and APIs.
5. Inter-Agent Orchestration for the AI Market
A significant trend is the rise of systems in which multiple AI agents collaborate to solve complex problems. This requires sophisticated orchestration to manage agent interactions.
6. Focus on Trust, Ethics, and Governance
As AI agents handle more sensitive information, ensuring robust data privacy and security measures becomes critical. Organizations recognize the need for clear guidelines and governance frameworks for developing and deploying AI agents.
7. Democratization and training for better development
The emergence of platforms that enable individuals and companies to create and deploy AI agents without extensive programming knowledge or experience will drive wider adoption.
The problem lies in building confidence in the accuracy of the final results.
Currently, several experts emphasize the importance of creating better prompts for instructions (providing better instructions, reasoning steps, context, examples, output formats, etc.). However, the problem does not end there. While it is important that the human user knows what to ask for and how to ask for it, it is also essential that the data used to train the model is high quality to achieve accurate responses and quality conversations. Additionally, the construction of this multi-agent model must be robust.
The common problem of «hallucinations» arises when AI systems frequently produce false information because users lose confidence in their reliability and hesitate to use them for critical tasks.
Relying on hallucinated information can lead to poor strategies and negative outcomes. How can these errors be corrected? Error rates depend heavily on the specific AI model, its architecture, the size and quality of its training data, and the task it performs. Currently, AI models are constantly improving, so error rates can change rapidly over time.
Some studies indicate that generative AI tools, including LLMs, generate false statements a significant percentage of the time (e.g., up to 25% in some categories). Although accurate, universally applicable statistics on hallucinations in AI are still being developed, the problem is significant and well-documented. Error rates vary considerably depending on the AI system, its training, and the task. Continued research and development focuses on mitigating hallucinations to build more reliable AI systems.
Toward Robust AI Products in Oil and Gas
A McKinsey report estimates that the global AI market for the energy sector will reach $50 billion by 2025. According to a recent Ernst & Young survey, 92% of oil and gas companies worldwide are investing in AI or plan to do so within the next five years. Meanwhile, 50% of oil and gas executives report using AI to address challenges throughout their organizations.
7Puentes currently develops robust products that focus on user needs and are increasingly sophisticated, flexible, and customizable based on specific customer requirements. The oil and gas sector is increasingly relying on AI solutions to improve performance.
If you are interested in learning more about this topic, you can contact our business specialists.
