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Agentic AI is a type of artificial intelligence capable of interpreting information, planning steps, and executing actions within defined limits to achieve a specific goal. Unlike traditional generative AI, which responds to a request or instruction, Agentic AI can operate across processes: it consults data, uses tools, makes decisions, and completes tasks autonomously or in collaboration with human agents.
Agentic AI-based AI agent platforms such as Inagent give companies the ability to establish a new way to manage customer service, sales, and post-sales processes with greater autonomy, traceability, and capacity for action. In this article, we’ll explain what Agentic AI is, how it differs from generative AI or GenAI, which use cases are already being applied, and how to start implementing it with a practical, measurable approach.
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What Is Agentic AI?
Although the term Agentic AI has become popular in English, it refers to artificial intelligence systems designed for action: systems capable of understanding context, deciding the next steps, and executing tasks in connection with tools and data to run end-to-end processes.
Its main difference from more traditional models, which need more exact instructions to move forward, lies in its degree of operational autonomy. It is not limited to generating a response; it can move through a workflow to solve a specific need.
In customer experience, this difference is easy to see:
- A traditional bot follows a defined flow.
- A bot powered by generative AI or GenAI can generate more natural responses, but it usually depends on instructions or preconfigured flows.
- An agent based on Agentic AI can decide what it needs to do to resolve an interaction: ask for a specific piece of information, consult a knowledge base, access a CRM, create a ticket, update a request, or escalate the case if it detects that human assistance is required.
This concept is related to the idea of agency, meaning the ability of a system to act toward a goal. In CX, that autonomy makes it possible to move from isolated interactions to complete processes: managing a complaint, qualifying a lead, rescheduling an appointment, supporting an admissions request, or following up on a payment.
According to Gartner, by 2028, 15% of day-to-day work decisions will be made autonomously by Agentic AI systems. With this progress, we are entering a new stage where AI agents can act as operational collaborators, capable of transforming productivity and traditional service models.
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Agentic AI vs. Generative AI: main differences
The concepts of generative AI and Agentic AI come from the same root, but their capabilities and focus are different:
- Generative AI helps create content or assist with specific tasks.
- Agentic AI adds a layer of autonomy: it can reason toward a goal, decide steps, and execute actions connected to business systems.
Let’s take a closer look at what the evolution from generative AI to Agentic AI implies:
1. From Individual Tasks to Complete Processes
- Generative AI systems are very useful for specific tasks, such as summarizing conversations, classifying messages, or drafting responses. However, when a process requires several steps, they usually need human supervision.
- Agentic AI expands these capabilities and connects them to more complex business processes. It can break a request down into steps, interpret documents or images, consult internal systems, validate conditions, execute an action, and communicate the result to the customer. In this way, it can cover complete processes within the value chain.
2. From Assisted Execution to Autonomous Management with Limits
Autonomy is one of the most important differences:
- A bot with generative AI needs underlying flows that tell it what to do at each moment.
- An AI agent can identify when to act, decide which step makes sense next, and complete a task within a previously defined framework. This autonomy does not mean a lack of control. Agentic AI works best when it operates with clear rules, defined permissions, human supervision, and protocols for transferring management to a person when the case requires it.
3. Context, History, and Human Supervision
Another key difference is the ability to maintain context and use external tools:
- A bot with generative AI can access knowledge bases, but its connection to more complex systems such as CRM is more limited.
- An agent based on Agentic AI relies on knowledge bases, but also on APIs that allow it to access internal systems and customer data to act with greater precision.
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Agentic AI use cases in companies
Agentic AI makes particular sense when a company manages repetitive, high-volume processes that are closely related to customer experience. A safe way to integrate it is to start with scenarios that combine three conditions:
- They consume time.
- They affect an important business KPI.
- They can be automated in a reasonably simple way.
Here are some use cases by industry:
In all these scenarios, the value of Agentic AI lies in connecting conversation, context, and action to resolve complete processes in collaboration with the human team.
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How to start implementing Agentic AI in your company
Agentic AI alone does not guarantee results: it needs to be connected to specific processes, available data, and well-defined business goals.
The MIT report The GenAI Divide: State of AI in Business 2025, based on more than 300 public AI initiatives, states that 95% of organizations are not seeing a return on their GenAI investments. The problem often lies in the project approach: solutions do not adapt well to workflows, do not incorporate operational feedback, or do not fit into teams’ daily routines.
To avoid falling into that 95%, it is advisable to start with specific, measurable use cases connected to business needs. At Inconcert, we start from the following evaluation model:
- Pain: How painful is this process today? Review interaction volume, repetitiveness, cost per contact, and customer frustration.
- ROI: How much does it impact the business? Consider cost reduction, conversion improvement, NPS, generated revenue, or productivity.
- Fit: Is it a good fit to start now? Analyze available data, whether the process is defined, whether there is a clear owner for the project, and whether implementation complexity is manageable.
The sequence would be:
1. Choose a specific process
Agentic AI works best when it starts with bounded, repetitive processes that have a clear beginning and end: frequently asked questions, scheduling, payment follow-up, lead qualification, or ticket creation.
2. Prioritize by business impact
Not all use cases have the same value. The ideal combination is high volume, operational impact, and a real ability to measure results. A good first case should demonstrate results without requiring a complete transformation of the operation.
3. Assign a business-side owner
This owner validates criteria, reviews results, contributes operational knowledge, and helps adjust the AI agent’s behavior based on what happens in real conversations.
4. Measure results from the start
With Agentic AI, it is important to go beyond usage or adoption and measure KPIs that help determine whether the agent is resolving the process correctly. Examples include autonomous resolution, transfers with context, cost per contact, first response time, conversion, and satisfaction.
5. Iterate before scaling the AI Agent
A good implementation does not need to cover every possible case from day one. The recommended approach is to launch an AI agent applied to a clear use case, review conversation history, adjust criteria for human intervention, and gradually expand based on the lessons learned.
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Inagent and Agentic AI applied to Customer Experience
Inagent is an AI agent solution for companies that makes it possible to resolve complete customer service, sales, and post-sales processes autonomously and in collaboration with human teams.
This Agentic AI-based platform enables companies to deploy AI agents capable of understanding natural language, consulting information, operating across different channels, executing actions, and transferring conversations to the human team when the case requires it.
In addition, Inagent is part of Inconcert’s omnichannel customer experience ecosystem. It can work connected to solutions such as Inconnect (omnichannel contact center), Infunnel (CRM and marketing automation), Inspeech (Speech Analytics), and Inteam (workforce management system), so that Agentic AI has context, traceability, and continuity within each business process.
If you want to see how AI agents would fit into your customer service, sales, or post-sales processes, we encourage you to request a free demo and one of our experts will help you define it.
Frequently asked questions about Agentic AI