Artificial intelligence has transformed the way businesses interact with the world. From chatbots to voice assistants, automation is everywhere. But a new generation of AI is rewriting the rules:agentic AI. More than just a conversational tool, it is capable of reasoning, making decisions, and acting autonomously to achieve complex goals. It is a major paradigm shift that is redefining the future of work and innovation in business.
What is agent-based AI? Definition
Agent-based AI refers to a new generation ofartificial intelligence based on intelligent agents capable of:
- Perceive their environment (information, data, context).
- Think things through and make decisions to analyze the situation.
- Work independently.
- Continuous learning throughexperience.
Unlike "traditional" chatbots, which simply answer questions, these autonomous agents can execute processes, collaborate with one another, and interact with various information systems to solve real-world problems.
Today, most organizations are still exploring the early stages of this technology’s maturity. The ultimate goal—multi-domain agents capable of complex orchestration—remains an exciting prospect.
The Benefits of Agent-Based AI for Businesses
Beyond simple automation, agent-based AI is a powerful driver of innovation. It is the ability of your intelligent systems to learn and act that will unlock growth potential.
- Increase the value your teams add: Agent-based AI tacklescomplex and low-value-addedtasks, such as managing administrative files or sorting emails. This frees up your employees—the human agents—to focus on tasks that require informed decision-making, creativity, or advanced customer service.
- Improving overall performance: Seamless process automation eliminates bottlenecks and accelerates workflows. Anintelligent agent can coordinate actions across multiple departments inreal time , from the initial customer interaction to the delivery of the solution.
- Transforming the customer and employee experience: Behind the scenes, agent-based AI streamlines user journeys. For customers, this means immediate, personalized assistance that goes far beyond FAQs. For employees, it means instant access to the right information and simplified tasks.
The 4 maturity levels of agent-based AI: Where does your company stand?
Agent-based AI should be viewed as a gradual process. The deployment of an intelligent agent occurs in stages, with each stage bringing an additional level of autonomy and efficiency.
Step 1 – Basic automation with simple rules
Initially, agent-based AI takes the form of FAQ chatbots or automated scripts that apply predefined rules to automate tasks. They are capable of solving simple problems and streamlining specific tasks.
These tools are an accessible first step toward digital transformation and offer immediate benefits in terms of operational efficiency.
Example: Answering frequently asked questions, retrieving specific information, or streamlining administrative procedures.
Step 2 – Support and Smart Search Agents
At this stage, intelligent agents become capable of searching for, analyzing, and synthesizing information to assist teams. They act as co-pilots, preparing decisions without acting on their own. This is a key step that combines AI expertise with human oversight.
Example: Proposing multiple financial scenarios, identifying trends in a corpus of documents, or supporting decision-making based on multiple sources.
Step 3 – Orchestration in a business domain
This is the stage where many companies currently find themselves. AI agents are capable of managing an entire workflow in a specific domain, handling complex tasks from start to finish. Their ability to integrate with existing systems makes it possible to transform entire processes.
Examples:
- Processing an insurance claim.
- Managing an IT ticket.
- Automating a purchase order.
This means:
- Integration with systems via APIs.
- Access and data governance.
- Monitoring of AI agents and ongoing updates.
Step 4 – Multi-agent orchestration (future plans)
The final level involves a system in which multiple autonomous agents collaborate across various business domains (CRM, ERP, HR, logistics, marketing). This vision of an interconnected AI ecosystem opens up unprecedented opportunities for efficiency.
Example: Automatically synchronize logistics, inventory, and local marketing campaigns using a network of interconnected AI agents.
At present, this phase is still in the experimental stage: interoperability standards, governance, and security still need to be further developed.
Where do we really stand?
In reality, most companies fall somewhere between Stage 2 and Stage 3.
- Steps 1 and 2 are already widely used and offer immediate benefits.
- Step 3 involves actual implementations across targeted business processes.
- Step 4 is still in the medium-term planning stage.
Agent-based AI should therefore be viewed as a gradual process, not as a ready-made solution available immediately.
Examples of practical applications of agent-based AI
For HR departments
In recruitment, an AI agent can analyze candidate profiles, screen resumes, and identify talent independently. It can also manage the onboarding of new employees by automating access to platforms and answering initial questions about company policies.
For IT departments
An intelligent agent can monitor a network in real time to detect threats and respond to them autonomously. It can also automate the handling of common support tickets, such as password reset requests, by interacting directly with the company’s systems.
For Customer Service and GRU
An autonomous agent can proactively manage customer disputes by identifying potential issues (e.g., late deliveries) even before a customer reports them. It can also optimize the customer experience by personalizing support and providing accurate information without human intervention.
How to Create AI Agents in a Business Setting?
The integration ofagent-based AI is not just a matter of technology; it is, above all, a strategic initiative. To implement anautonomous agent project, the first step is to choose a learning approach that is tailored to your needs and your data.
Key steps for a successful implementation
- Define the business objective: Clearly identify the specific task you want to automate, such as IT ticket management or the validation of specific forms for HR.
- Select the method: Identify the most appropriate type of learning (reinforcement, supervised, or unsupervised) based on the complexity of the task to be performed.
- Build and deploy: Agent development requires a rigorous testing environment. Deployment is a critical phase that must be followed by ongoing updates.
- Monitoring: Even after it goes live, the agent requires human oversight. Its learning process over time must be monitored to ensure reliability and security.
Reinforcement learning: autonomy through experience
In this approach, the agent is placed in an environment where it learns through trial and error. It is rewarded for correct actions and penalized for incorrect ones. Reinforcement learning gives the agent a high degree of autonomy to discover the best strategy, making it ideal for dynamic and complex environments.
Supervised learning: the agent that learns by example
This technique relies on the use of a large labeled dataset to train the agent. The agent learns to associate an input ( e.g., an image of a computer) with an expected output (e.g., the word “computer”). Supervised learning is the method of choice for classification and prediction tasks .
Unsupervised learning: the agent that discovers patterns on its own
Inunsupervised learning, the agent explores a set of unlabeled data to identify correlations and hidden patterns. The goal is to organize the data and uncover insights without human intervention. It is often used for customer segmentation orbehavioral analysis.
Take action with the Wikit platform
Wikit has embarked on an ambitious R&D initiative to evolve its Wikit Semantics platform into an agent-based platform. The goal is clear: to build a next-generation agent-based infrastructure designed to combine autonomy, security, and sovereignty.
Thanks to its no-code approach, Wikit Semantics is poised to make the creation and deployment of AI agents accessible to everyone, with no technical skills required.
Ultimately,agent-based AI ushers in a new era of automation and artificial intelligence. Much more than just a tool, it represents a gradual evolution, from simple automation scripts to autonomous agents capable of orchestrating complex processes. Understanding its maturity levels, concrete benefits, and implementation methods is essential for companies looking to adopt it.
The future of work depends on collaboration between AI agents and humans, in order to automate low-value-added tasks and sustainably improve productivity.
