Agentic AI is based on autonomous AI agents capable of managing an objective end to end.
While the term is still recent and sometimes used differently depending on the context, some use “agentic AI” and “AI agents” interchangeably, the core idea remains the same. It refers to AI systems that can act, decide, and adapt without constant human intervention.
Agentic AI goes beyond simple task automation or predefined workflows. The agent understands a request, builds an action plan, selects the right tools, and adjusts its actions based on the results obtained. This approach builds on principles already present in decision systems and robotics, but with a more advanced ability to anticipate and operate in dynamic environments. It marks a shift from execution to a control and orchestration logic.
This approach becomes even more powerful in multi-agent systems. In this context, several AI agents can collaborate, distribute roles, share information, and adjust their actions based on each other’s behavior. It is no longer just about a single intelligent system acting alone, but about a network of agents working together to handle more complex tasks and improve decision-making quality.
Example of a multi-agent system in insurance
To understand what this changes in practice, let’s take the example of an insurance claim declaration.
A policyholder experiences water damage and needs to act quickly. In a traditional journey, they have to navigate menus, repeat their information, and wait for a customer service agent to take over. With agentic AI, the experience is immediate. From their phone, an AI agent understands the intent, initiates the process, and guides the user step by step. The claim is started within minutes, with no friction.
This fluidity relies on multi-agent orchestration. A first agent collects the data (type of incident, photos, context), a second checks compliance and interacts with the policyholder to complete any missing information, and a final synthesis agent structures the case file and forwards it to the claims handler, who then has all the necessary information for fast processing. In other words, the AI doesn’t handle a single task, it coordinates multiple agents to manage the claim end-to-end.
The claims handler then takes over with a file that is already qualified, contextualized, and ready to process. They can focus on support, using empathy and reassurance to assist the policyholder. The AI does not replace internal teams, it enhances their ability to act.
This experience extends across all touchpoints. The policyholder can start the process from an email, a website, an app, a QR code, or voice, and benefit from the same continuity throughout the journey. At the same time, the AI integrates with existing systems (CRM, customer service platforms, telephony), enabling real-time data flow. This results in faster, more reliable, and truly omnichannel claim management, with measurable gains in both customer experience and operational performance.
Business Process Management have long been structured around a sequence of well-defined tasks, where each step depends on the previous one, with fixed rules and often rigid workflows. This model has made it possible to efficiently organize operations, but it quickly reaches its limits as soon as the context becomes more complex or variable. Each exception requires human intervention, and every specific case slows down the overall process.
With agentic AI, the logic shifts. Instead of thinking in terms of tasks to execute, the focus moves to an objective to achieve. The agent no longer follows a predefined path; it is given a global goal, analyzes the situation, and builds the necessary steps itself based on the context. If information changes, if a step fails, or if a better option emerges, it adjusts its strategy without starting over.
Thanks to advanced sensing capabilities, agents can understand and analyze their operational environment. Their action components, known as Effector, then allow them to act concretely within that environment. They can continuously adapt their actions based on feedback, following a logic of ongoing improvement.
In most organizations, a large part of the workload still relies on intermediate decisions. These are all the day-to-day actions such as validating information, sorting requests, or routing them to the right contact. Individually, these decisions may seem simple, but taken together, they represent a significant operational burden and often slow down processes.
Agentic AI can handle these micro-decisions autonomously, leveraging context, available data, and defined objectives. An AI agent can qualify a customer request, prioritize tickets, select the most relevant communication channel, or automatically trigger the appropriate actions.
Beyond simple automation, the real shift lies in the ability to make decisions dynamically. The agent does not follow a single rule, it adapts its choices based on the situation, urgency level, user profile, or interaction history. It continuously arbitrates, whereas these decisions were previously handled by teams. As a result, employees are no longer mobilized for simple or repetitive decisions and can focus on more complex, higher-value tasks where human judgment remains essential.
Many organizations still operate in silos. A customer may start an interaction over the phone, continue it via email or chat… and the information does not always follow. This leads to broken journeys and a fragmented experience.
Agentic AI is not designed to operate in isolation. It interacts with multiple systems simultaneously, whether it is a Customer Relationship Management, business tools, or communication platforms. It plays a cross-functional coordination role, ensuring that information flows, triggering the right actions, and synchronizing tools in real time.
This means that an action initiated in one channel can be immediately understood, enriched, and continued in another, without any loss of information or disruption in the journey. Organizations therefore move from a stack of disconnected tools to a true omnichannel customer experience, delivering a seamless and consistent experience.
One of the key strengths of agentic AI is its ability to learn continuously.
It does not simply execute an action and move on. It observes what happens, analyzes the outcomes, and draws insights to improve future decisions.
For example, it can identify which journeys perform best, detect friction points, or spot situations where users drop off. Based on this, it adjusts its behavior. This results in more efficient journeys, fewer errors in request handling, and a gradual improvement in resolution rates.
By interacting with its environment, the agent continuously accumulates experience, enriching its Knowledge Base. Leveraging Machine Learning algorithms, it evolves in real time, constantly improving its performance and refining its action strategies over time. The more it is used, the more effective it becomes.
Integrating agentic AI into business processes goes far beyond simple performance gains. It fundamentally transforms how organizations operate on a daily basis.
By automating complete workflows and handling part of the decision-making, AI helps reduce average handling time, absorb higher volumes, and improve overall team productivity.
There is also a direct impact on costs. Reducing manual tasks, rework, and error rates leads to lower processing costs and better resource control.
From a customer experience perspective, the impact is reflected in key satisfaction metrics. First Contact Resolution (FCR) improves, response times decrease, and journeys become more seamless, contributing to higher Net Promoter Score and lower abandonment rates.
Reachability is also strengthened. With continuous request handling and better guidance from the first point of contact, answer rates increase and waiting times decrease.
Agentic AI also enables more precise operational steering. Companies gain deeper insights into customer journeys, friction points, and process performance, making it easier to prioritize actions and optimize continuously.
Ultimately, the value lies not only in overall gains, but in the measurable improvement of key KPIs at every stage of the customer journey.
Agentic AI opens up new possibilities, but to fully leverage it, it must operate within a clear and structured framework.
The goal is not to limit its use, but to establish the right foundations so it can be used in a reliable, consistent, and controlled way over time.
In practice, this starts with implementing governance rules. This involves defining how agents make decisions, which data they rely on, and within what scope they are allowed to act. It also means clarifying the objectives assigned to them, the expected success criteria, and the boundaries they must not exceed. The more explicit the framework, the more effective and aligned agents are with business priorities.
From an operational standpoint, this helps eliminate ambiguity. The agent knows when to act, how to prioritize, and how far it can go. At the same time, teams retain a clear view of what is automated, what remains under human control, and how decisions are made.
It is also essential to maintain visibility over the actions performed. The objective is not to control every decision, but to understand the choices made, analyze outcomes, and adjust when necessary. This makes it possible to trace journeys, identify decision logic, and measure the real impact on performance. For example, organizations can analyze why certain requests are handled more effectively than others or detect friction points within a journey. This level of visibility allows for continuous adjustments, refining rules, improving journeys, and optimizing strategies, without having to redesign the entire system. The goal is not to monitor AI, but to learn from it and make it increasingly relevant over time.
Data management also plays a central role. Behind every decision made by an AI agent lies data that guides its choices. The more reliable, up-to-date, and well-structured this data is, the more relevant the decisions will be. Conversely, incomplete or poorly used data can limit the agent’s effectiveness. This is why it is essential to focus on data quality from the outset and over time. This involves regular updates, proper structuring, and consistent use across different tools. Security is another key consideration. Protecting data while ensuring it remains accessible to the right systems allows organizations to balance performance and compliance.
In this context, the concept of trustworthy AI becomes essential. The goal is not just to have a high-performing agent, but one that can be reliably used on a daily basis. This translates into decisions that are understandable and explainable, actions that are traceable, and a logic clearly aligned with business objectives. To support this, approaches such as LLM as a Judge are becoming increasingly common. The principle is simple: one AI evaluates the responses or decisions of another AI. This makes it possible to verify whether actions are relevant, consistent, and aligned with the intended objective.
The agent does not only act, it is also continuously evaluated. This is precisely what enables greater reliability without adding unnecessary complexity to processes.
Finally, agentic AI is most effective when integrated into a collaborative model with teams. Automation handles part of the workload, while employees focus on more complex or strategic situations. Beyond the technology itself, success relies on working with a partner capable of understanding business challenges, integrating into existing systems, and supporting long-term transformation such as DialOnce. A strong partner does not just provide a tool; they help structure processes, define the right use cases, and monitor performance. This is also what accelerates results, as teams are better supported and customer journeys are optimized more quickly.
Agentic AI marks a significant shift in how business processes are designed. More than just an automation lever, it introduces a new way to structure and manage daily operations.
In practical terms, organizations are gradually moving away from rigid models toward more flexible systems that can adapt, make decisions, and evolve based on context. This leads to workflows that are more seamless, more consistent, and easier to manage, with a more precise understanding of performance through directly actionable metrics.
In this context, companies that structure their approach, rely on the right tools, and implement clear operational governance are able to sustainably improve process efficiency while enhancing the quality of customer journeys. Ultimately, the goal is not just to optimize what already exists, but to adopt a more agile and coherent way of operating across the entire organization.
