--- title: What is agentic AI? date: 2025-09-08T10:55:01+02:00 author: Administrator canonical_url: "https://www.xploregroup.be/en/insights/what-is-agentic-ai" section: Insights --- # What is agentic AI? ## Data & AI ![Agentic AI](https://cdn.craft.cloud/5fa59528-9049-4864-a4f2-a0667591b3bd/assets/img/Agentic-AI.jpg) ## The rise of AI systems that can think, plan and act on their own Artificial intelligence has been on the rise for years. We use AI in search engines, recommendation algorithms, language models and chatbots – often without realizing it. But the way we interact with AI is changing quickly. More and more, we no longer see AI as just a smart *tool*, but as an active *partner*. Welcome to the era of **agentic AI**. In this blog, we explain what agentic AI actually is, how it differs from traditional AI and why this technology will have such a major impact on how we work, create and make decisions. ## The shift: from reactive AI to autonomous agents Most AI systems we know today are **reactive**. You ask a question, the system provides an answer. Or you feed in data and the algorithm predicts an outcome. Think of ChatGPT, Netflix recommendations or spam filters: useful, but always dependent on human input and direction. **Agentic AI** takes things a step further. These systems function as **autonomous 'agents'**: digital entities that receive an assignment, plan how to execute it and then take action on their own. They are no longer just reactive, but **goal-oriented, independent and adaptive**. This means that an agentic AI does not need to be guided at every step. You give it a goal and it figures out the best way to achieve it – including gathering information, making decisions and adjusting when necessary. **Important to note**: in practice, we almost always build in a **“human in the loop”**. An agent rarely operates completely autonomously. However, you can gradually give it **more freedom** as the system earns trust and delivers better results. ## What makes an AI ‘agentic’? To function as an *agent*, an AI needs several specific capabilities: ### 1. Goal orientation An agentic AI works toward an **end goal**. It doesn’t just get a task (e.g. “generate a text”), but a broader assignment (e.g. “develop a content strategy for target audience X”). ### 2. Reasoning and planning The system can analyze complex problems, break tasks down into steps, set priorities and outline an approach. ### 3. Autonomy within boundaries Agentic AI operates **independently**. It makes decisions without constantly asking for input, but always within predefined limits (such as business guidelines, ethical principles or technical constraints). ### 4. Long-term memory and context awareness Whereas many classical AI systems are limited to a single session, an agentic AI can retain information over time and use context to adapt its behavior. ### 5. Adaptability and self-correction The AI learns from feedback or changing circumstances and can improve itself while working. ## A concrete example: from prompt to project manager Suppose you ask an AI to help with a product launch. A traditional AI system could help by generating some slogans or drafting an email. But an agentic AI goes much further. Give it the assignment “Ensure a successful launch of product X to a young audience” and the system will: - Decide which channels are suitable (TikTok, Instagram, email, influencers…), - Develop a plan with deadlines and deliverables, - Generate and distribute the right content across channels, - Analyze results and adjust where needed, - And keep you updated with summaries and reports. **Success conditions:** this only works if the agent has **enough context** about the **company**, the **target audience**, the **channels** and **past campaigns**. In addition, you build in **checkpoints**: for example, human approval before going live, budget caps and **logging** for audit & compliance. The result is a digital project manager or growth marketer who takes initiative, but **never acts outside the agreed boundaries**. ## What does this mean for organizations? The rise of agentic AI has major implications for businesses, governments and organizations. By allowing AI systems to plan and execute independently, we can: - **Work more efficiently**, as repetitive and complex processes are automated; - **Innovate faster**, as AI continuously tests and improves alternatives; - **Make better decisions**, thanks to ongoing analysis of context, data and feedback; - **Free up people** to focus on creativity, empathy, and strategy. But with that power also comes responsibility. If AI systems are allowed to act independently, we must set clear boundaries. How do we ensure they operate within ethical and legal frameworks? How do we maintain control without stifling innovation? ## Agentic AI is the next step Agentic AI marks a **new phase** in what AI can mean for us. These are no longer passive assistants, but active digital colleagues who can think, build and evolve alongside us – **provided they are given the right context** and **operate within clear boundaries**. In this blog series, we will take you step by step through the different types of AI – from generative AI to agentic AI. Each type has its role, its capabilities and its limits. Together, they form the building blocks of the future of work, innovation and technology. Curious how this technology can be applied in practice? Or how your organization can start experimenting with agentic AI? Follow our series – or get in touch with us for an exploratory conversation. ### Contact us [Contact us](https://www.xploregroup.be/en/contact) ## Related insights [View all insights](https://www.xploregroup.be/en/insights) ### AI-driven solutions for efficiency and innovation in the food industry #### Data & AI #### Innovatie #### Innovation Noa Vanderhaegen ![Healthy Unhealthy Food](https://cdn.craft.cloud/5fa59528-9049-4864-a4f2-a0667591b3bd/assets/img/Healthy-Unhealthy-Food.jpg) AI (artificial intelligence) is having a significant impact on the **food industry**. It enables companies to better understand customer needs, deliver personalised content and automate processes such as product descriptions and document processing. Moreover, AI provides more objectivity in quality control, leading to increased efficiency. Despite the many possibilities AI offers, it also raises questions. What are the **practical applications** of AI for your business? And how do you decide which priorities are important when implementing this technology? On this page, we’ll give some **practical examples of AI applications** we have successfully implemented at Xplore Group. These use cases show how AI not only makes work easier, but also promotes innovation and efficiency. Get inspired by the possibilities of AI and discover how your team can use the power of technology to stay ahead in a competitive market. #### Practical use cases ### Food quality control At one of our customers, we are currently implementing an **advanced quality control process using AI technology.** Traditionally, quality control is often a subjective process where human reviewers visually inspect products. This can lead to variations in the assessment depending on the experience and insight of the person performing the inspection. Using **Computer Vision, a branch of artificial intelligence that enables computers to analyse and interpret visual information**, we are transforming this process. Computer Vision allows us to **objectify visual inspections by applying fixed criteria and algorithms** to each product being inspected. This allows us to achieve a more consistent and accurate assessment. The benefits of this approach are clear: **improved accuracy, increased efficiency and less reliance on human interpretation**. In addition, the use of AI makes it possible to detect abnormalities or defects that are difficult for the human eye to detect. This leads to a higher quality level of the final products and helps our customer meet even the strictest quality standards. **Conclusion** - Increase operational efficiency and reduce errors. - Detect deviations or defects faster. - Increase the quality of your end products. ### B2B automation of orders In B2B, automating orders can be an essential aspect to increase operational efficiency. B2B customers place orders through various channels, such as a web portal or via representatives. In doing so, it is noteworthy that **orders still often arrive via mail or PDF documents**. Previously, a team member had to manually read those PDFs and type the information into the ERP system. **By deploying AI, you can have those PDFs automatically read, interpreted and extract all the relevant information** needed to process the orders. That data is then **automatically loaded into the ERP system**. Mainly in the initial phase, it is still necessary to manually check the results, especially in case of ambiguity, but also to identify any inaccuracies. These are then flagged for further checking. You can also choose to **place the orders directly in your e-commerce portal, instead of traditionally working through an ERP.** Then, based on the customer information in the order, a customer account can immediately be created in the e-commerce portal. This allows them to fill their next order directly in the e-commerce portal. This **optimised ease of use** has the potential to trigger a behavioural change in your customers. **Conclusion** - Increase operational efficiency and reduce errors. - Make the ordering process easier and faster for your customers. - Ensure seamless integration of orders into your systems. ### Predictive AI So far, we have mainly talked about generative AI. But you can also use **the predictive power of AI** in sales and marketing. Machine learning (ML) and artificial intelligence (AI) give us the ability to **combine all sales data from past years with current trends in the market**, such as API data. By using different data sources, you can **make predictions about customers’ buying behaviour** through predictive AI. A good example is integrating **a weather API, where historical weather data is included in the analysis.** This goes beyond just comparing week to week: it also includes the impact of external factors such as holidays, exact days of the week … As a result, you can **more accurately predict the impact of weather on consumer buying behaviour and more easily anticipate changes in demand** for specific products. You can even predict when is the right time to schedule a specific marketing campaign. What happens if we give a 10% discount on part of our range this weekend? By analysing historical data, AI allows you to make a very accurate prediction. Based on that data, companies can also better anticipate inventory levels. This allows you to act proactively. Allowing companies to better meet customer demand and avoid excess stock. **Conclusion** - Improve your marketing strategies with accurate forecasting. - Optimise your inventory management and reduce costs. - Anticipate customer needs and improve customer satisfaction. ### Hyperpersonalisation of email marketing Email marketing personalisation is nothing new: adding your customer’s name in the subject of your email or matching products based on purchase, it’s been done for years. With AI, you can go a step further: hyper-personalisation! It involves **generating personalised pieces of text or images based on AI prompts that use customer data**. This way, you make each newsletter unique and relevant to the recipient. In a B2B context, you can **map the history of your customer or prospect and use generative AI to create a tailored message.** You can also use the predictive power of AI to calculate the optimal time when to contact your customer or prospect and what message or action to offer then. Hyperpersonalisation makes every customer interaction more valuable. **Conclusion** - Increase customer engagement with personalised emails. - Maximise the effectiveness of your marketing campaigns. - Build stronger customer relationships through relevant and timely communication. ### Content creation and AI Content creation is often the first thing people think of when using AI. This is because AI allows you to **easily scale your content efforts**, allowing you to generate content quickly and on a large scale. But let’s take it a step further! Integrations with a Product Information Management (PIM) system are a valuable next step. This goes beyond just generating product descriptions: it extends to **a full SEO strategy**. We can generate meta titles and meta descriptions that are SEO-friendly, and we can enrich product information with your brand values directly in your PIM. This guarantees unique texts, especially important for manufacturers who need to be GS1-compliant, and whose products are also sold at different retailers. How do you differentiate yourself from other online players offering exactly the same products? By writing AI prompts in such a way that you get unique and SEO-optimised content! **Conclusion** - Save time and resources through efficient content creation. - Increase your online visibility with SEO-optimised texts. - Ensure consistent brand communication and compliance. #### Book a free inspiration session Want to discover how Artificial Intelligence can transform your business? During a free inspiration session, we will share real-life examples of successful Al implementations at Xplore Group. In doing so, we will demonstrate how Al can not only streamline processes, but also drive innovation and offer new opportunities for growth. 21021 AI-driven solutions for efficiency and innovation in the food industry #### AI (artificial intelligence) is having a significant impact on the **[food industry](https://www.xploregroup.be/en/digitalisation-in-the-food-industry/)**. It enables companies to better understand customer needs, deliver personalised content and automate processes such as product descriptions and document processing. Moreover, AI provides more objectivity in quality control, leading to increased efficiency.. Despite the many possibilities AI offers, it also raises questions. What are the practical applications of AI for your business? And how do you decide which priorities are important when implementing this technology? On this page, we'll give some practical examples of AI applications we have successfully implemented at Xplore Group. These use cases show how AI not only makes work easier, but also promotes innovation and efficiency. Get inspired by the possibilities of AI and discover how your team can use the power of technology to stay ahead in a competitive market. [Book a free inspiration session](#contact)#### Practical use cases Want to discover how Artificial Intelligence can transform your business? During **a free inspiration session**, we will share real-life examples of successful AI implementations at Xplore Group. In doing so, we will demonstrate how AI can not only streamline processes, but also drive innovation and offer new opportunities for growth. ## **Book a free** **inspiration session** **Contactpersoon Voedingssector:** Noa Vanderhaegen noa.vanderhaegen@xploregroup.be +32 479 93 21 88 ### The role of a PIM specialist #### Data & AI #### Technology & Platforms ![Andrea Piacquadio Photo](https://cdn.craft.cloud/5fa59528-9049-4864-a4f2-a0667591b3bd/assets/img/Andrea-Piacquadio-Photo.jpg) 11866 The role of a PIM specialist #### Imagine this: you have decided to centralise and structure your product data. This can be done through a PIM (Product Information Management) system. To use the maximum potential of this complex tool, it can be useful to bring in experts with the right knowledge and background: a PIM specialist. He or she is able to seamlessly connect the company's business needs with the technical aspects of the software. #### The role of a PIM specialist The role of a PIM specialist is to support the users of the PIM system in their daily tasks. He or she ensures that workflows are followed and that the system is well maintained and expanded when needed. The specialist is also the point of contact in case of possible problems. In addition, the PIM specialist is responsible for training users and is often involved in onboarding new business units to the system. By coordinating with various stakeholders, the specialist understands the customers' needs, allowing product information and processes to be optimised, which in turn also boosts sales. A PIM specialist also contributes to the preparation and execution of data migration and can help the business set up reports and analyses. He or she is an important link between the technical department and the business, which often leads to smoother communication between the different departments. #### A successful implementation thanks to a PIM specialist The average employee often has limited experience with complex data processing. By engaging a PIM specialist, employees and teams can stay focused on their core activities. While the business focuses on customers and suppliers, the IT department takes care of the technical systems, and sales and marketing acquire new customers, the PIM specialist takes care of optimising data processing. With specific (sector) knowledge, he/she can quickly and efficiently perform evaluations to make improvements where needed. From experience we have noticed that a change in working methods often leads to resistance. It is the role of the PIM specialist to then act as a connecting factor between departments that sometimes have conflicting interests. For example, finding the right balance between elaborate administrative processes and the need to respond to trends fast. In this way, the PIM specialist can be a mediator to all departments involved. With his people and change management skills, he ensures that everyone is on the same page and working towards the same goals. ## Xplore Group's PIM specialists At Xplore Group we have several competence centers full of PIM experts with a total knowledge of more than 40 different packages. Furthermore, they are familiar with the data needs of most major retailers on the Benelux market, and have experience with a very diverse range of products in different sectors. They can join your internal teams smoothly and are able to communicate with all stakeholders according to their interests. In addition, we make sure that our experts give your internal staff sufficient explanation so that they can continue optimising in the future without wasting time. Basend on an article written by Xplore Group's competence center [Master Data Partners.](https://www.masterdatapartners.be/) **Could you use some help optimizing your PIM system?** **Our Xplorers can help.** [Contact us](https://www.xploregroup.be/en/contact) ### Why good project documentation is not an afterthought #### Data & AI #### Technology & Platforms Evelyn Van Roey ![Startup Meeting Business People Jan 31 2023](https://cdn.craft.cloud/5fa59528-9049-4864-a4f2-a0667591b3bd/assets/img/Startup-Meeting-Business-People-Jan-31-2023.jpg) #### Best practices for project documentation To ensure that documentation becomes an asset rather than a burden for your project, here are some best practices: **Planning and Preparation** - **Start Early**: Choose a style for documentation from day one and stick to it. - **Name the Owner**: For each segment of the documentation, an owner should be designated to take responsibility. **Structure and Content** - **Project Charter**: This document outlines the project's objectives, scope, stakeholders, and starting points, serving as its foundation. - **Consistency**: Maintain a consistent style and frequency when updating documentation and status reports. **Monitoring and Updates** - **Track Changes**: Every change, no matter how small, should be logged to understand retrospectively why certain decisions were made. - **Version Control**: This is crucial for keeping stakeholders informed on what's completed and what's still in progress. **Meetings and Communication** - **Meeting Notes**: These should be documented and shared as soon as possible to ensure alignment within the team. - **Testing Guidelines**: Create clear guidelines for user tests to smooth the acceptance process and ensure quality. #### Why is project documentation important? Starting a project without solid documentation is something we've all experienced, and it inevitably results in team confusion over objectives, requirements, and strategies. Well-structured documentation ensures: - **Clear Communication**: Documentation aligns all stakeholders on requirements, changes, risks, and the project's status. - **Knowledge Transfer**: Without existing documentation, a new project feels like a leap into the unknown. Good documentation significantly shortens the learning curve. - **Future Roadmaps**: Documenting decisions made during the project eases future decision-making and explains why certain choices were made. - **Risk Mitigation**: A well-documented project helps identify potential issues early on and allows for proactive steps to mitigate them. #### Conclusion Project documentation is not a peripheral concern; it is an essential component of every successful project. It ensures clear communication, makes knowledge transfer more efficient, assists in future planning, and limits risks. Therefore, it is critically important not to ignore it or procrastinate. With the right approach and best practices, documentation can become a powerful tool for unlocking the success of your project. #### Can you use some help with project management? Our Xplorers know what to do! [Contact us](https://www.xploregroup.be/en/contact) #### Related insights [View all insights](https://www.xploregroup.be/en/insights) 11211 Why good project documentation is not an afterthought #### For project managers, tight deadlines and hectic schedules are nothing new. However, an element that often gets overlooked amidst this chaos is project documentation. At Xplore Group, we understand that thorough documentation is not merely a detail but the secret ingredient that saves projects, enhances communication, and ensures successful outcomes. ### **Why is project documentation important?** Starting a project without solid documentation is something we've all experienced, and it inevitably results in team confusion over objectives, requirements, and strategies. Well-structured documentation ensures: - **Clear Communication**: Documentation aligns all stakeholders on requirements, changes, risks, and the project's status. - **Knowledge Transfer**: Without existing documentation, a new project feels like a leap into the unknown. Good documentation significantly shortens the learning curve. - **Future Roadmaps**: Documenting decisions made during the project eases future decision-making and explains why certain choices were made. - **Risk Mitigation**: A well-documented project helps identify potential issues early on and allows for proactive steps to mitigate them. ### **Best practices for project documentation** To ensure that documentation becomes an asset rather than a burden for your project, here are some best practices: **Planning and Preparation** - **Start Early**: Choose a style for documentation from day one and stick to it. - **Name the Owner**: For each segment of the documentation, an owner should be designated to take responsibility. **Structure and Content** - **Project Charter**: This document outlines the project's objectives, scope, stakeholders, and starting points, serving as its foundation. - **Consistency**: Maintain a consistent style and frequency when updating documentation and status reports. **Monitoring and Updates** - **Track Changes**: Every change, no matter how small, should be logged to understand retrospectively why certain decisions were made. - **Version Control**: This is crucial for keeping stakeholders informed on what's completed and what's still in progress. **Meetings and Communication** - **Meeting Notes**: These should be documented and shared as soon as possible to ensure alignment within the team. - **Testing Guidelines**: Create clear guidelines for user tests to smooth the acceptance process and ensure quality. ### **Conclusion** Project documentation is not a peripheral concern; it is an essential component of every successful project. It ensures clear communication, makes knowledge transfer more efficient, assists in future planning, and limits risks. Therefore, it is critically important not to ignore it or procrastinate. With the right approach and best practices, documentation can become a powerful tool for unlocking the success of your project. **Author:** Evelyn Van Roey, Project Manager at Xplore Group **Can you use some help with project management? Our Xplorers know what to do!** [Contact us](https://www.xploregroup.be/contact/) 24137 What is agentic AI? ## The rise of AI systems that can think, plan and act on their own Artificial intelligence has been on the rise for years. We use AI in search engines, recommendation algorithms, language models and chatbots – often without realizing it. But the way we interact with AI is changing quickly. More and more, we no longer see AI as just a smart *tool*, but as an active *partner*. Welcome to the era of **agentic AI**. In this blog, we explain what agentic AI actually is, how it differs from traditional AI and why this technology will have such a major impact on how we work, create and make decisions. ## The shift: from reactive AI to autonomous agents Most AI systems we know today are **reactive**. You ask a question, the system provides an answer. Or you feed in data and the algorithm predicts an outcome. Think of ChatGPT, Netflix recommendations or spam filters: useful, but always dependent on human input and direction. **Agentic AI** takes things a step further. These systems function as **autonomous 'agents'**: digital entities that receive an assignment, plan how to execute it and then take action on their own. They are no longer just reactive, but **goal-oriented, independent and adaptive**. This means that an agentic AI does not need to be guided at every step. You give it a goal and it figures out the best way to achieve it – including gathering information, making decisions and adjusting when necessary. **Important to note**: in practice, we almost always build in a **“human in the loop”**. An agent rarely operates completely autonomously. However, you can gradually give it **more freedom** as the system earns trust and delivers better results. ## What makes an AI ‘agentic’? To function as an *agent*, an AI needs several specific capabilities: ### 1. Goal orientation An agentic AI works toward an **end goal**. It doesn’t just get a task (e.g. “generate a text”), but a broader assignment (e.g. “develop a content strategy for target audience X”). ### 2. Reasoning and planning The system can analyze complex problems, break tasks down into steps, set priorities and outline an approach. ### 3. Autonomy within boundaries Agentic AI operates **independently**. It makes decisions without constantly asking for input, but always within predefined limits (such as business guidelines, ethical principles or technical constraints). ### 4. Long-term memory and context awareness Whereas many classical AI systems are limited to a single session, an agentic AI can retain information over time and use context to adapt its behavior. ### 5. Adaptability and self-correction The AI learns from feedback or changing circumstances and can improve itself while working. ## A concrete example: from prompt to project manager Suppose you ask an AI to help with a product launch. A traditional AI system could help by generating some slogans or drafting an email. But an agentic AI goes much further. Give it the assignment “Ensure a successful launch of product X to a young audience” and the system will: - Decide which channels are suitable (TikTok, Instagram, email, influencers…), - Develop a plan with deadlines and deliverables, - Generate and distribute the right content across channels, - Analyze results and adjust where needed, - And keep you updated with summaries and reports. **Success conditions:** this only works if the agent has **enough context** about the **company**, the **target audience**, the **channels** and **past campaigns**. In addition, you build in **checkpoints**: for example, human approval before going live, budget caps and **logging** for audit & compliance. The result is a digital project manager or growth marketer who takes initiative, but **never acts outside the agreed boundaries**. ## What does this mean for organizations? The rise of agentic AI has major implications for businesses, governments and organizations. By allowing AI systems to plan and execute independently, we can: - **Work more efficiently**, as repetitive and complex processes are automated; - **Innovate faster**, as AI continuously tests and improves alternatives; - **Make better decisions**, thanks to ongoing analysis of context, data and feedback; - **Free up people** to focus on creativity, empathy, and strategy. But with that power also comes responsibility. If AI systems are allowed to act independently, we must set clear boundaries. How do we ensure they operate within ethical and legal frameworks? How do we maintain control without stifling innovation? ## Agentic AI is the next step Agentic AI marks a **new phase** in what AI can mean for us. These are no longer passive assistants, but active digital colleagues who can think, build and evolve alongside us – **provided they are given the right context** and **operate within clear boundaries**. In this blog series, we will take you step by step through the different types of AI – from generative AI to agentic AI. Each type has its role, its capabilities and its limits. Together, they form the building blocks of the future of work, innovation and technology. Curious how this technology can be applied in practice? Or how your organization can start experimenting with agentic AI? Follow our series – or get in touch with us for an exploratory conversation. [Contact us](https://www.xploregroup.be/en/contact/)