What is Agentic AI? Separating Hype from Reality

Last Updated: 18th February 2026

by Chris Martin

Previously, we looked at how Netcall’s Liberty platform uses agentic AI frameworks to transform business operations. As the term is gaining a lot of traction across many sectors, it’s time to cut through the noise, so this blog delves deeper into what agentic AI truly involves and looks at what this technology really means for your organisation.

In Netcall’s guide about agentic AI, you’ll learn about:

1. What is agentic AI?
2. How does agentic AI work?
3. Agentic AI features
4. What are the benefits of agentic AI?
5. Agentic AI use cases
6. Agentic AI considerations and best practices
7. The future of agentic AI
8. Balancing technical excellence with accessibility

What is agentic AI?

Agentic AI refers to systems capable of autonomous decision-making and task execution without constant human oversight. Think of it as an efficient expert working on your task (that never sleeps).

Agentic AI also adapts to dynamic environments, learning and evolving over time. It can make decisions, adapt on the fly and handle curveballs, working through the more automatable tasks in the background and only bringing you in when you are needed. This capability to learn from experience and improve over time allows for more nuanced and efficient handling of complex tasks.

The allure and misconceptions around agentic AI

Everyone’s jumping on the AI bandwagon, and the rapid advancement of agentic AI has led to a surge in its adoption claims. But not all claims are created equal.

Many organisations tout agentic capabilities, yet often these are limited to basic automation or scripted responses. True agentic AI involves a higher level of autonomy and adaptability, characteristics that are not easily achieved. It’s like the difference between a GPS that just gives directions versus one that reroutes you around traffic, suggests better routes based on your driving habits and learns your preferences over time.

What is agentic AI vs generative AI?

This is the most common point of confusion. Generative AI is a technology and agentic AI is an architecture built upon it.

The primary goal of generative AI is the creation of text, code, images or summaries. Agentic AI focuses on the execution, like multi-step completion.

While the LLM is the core value in generative AI, it is the reasoning components of agentic AI. We will discover more key components of agentic AI below.

Agentic AI and agentic automation

The main difference lies in the level of predictability required. Agentic automation is achieved through a carefully coordinated and interconnected system of AI agents, robots and human collaboration.

Compared to traditional automation, which is rules-based and deterministic for high-volume, repetitive, and predictable tasks like monthly payroll, agentic automation is best for complex processes, messy data, and environments that require real-time judgment, such as dynamically rerouting supply chains during a disruption.

Agentic AI vs AI agents

The terms “AI agent” and “agentic AI” are often used interchangeably, but there is a clear distinction. You can think of an AI agent as a specific, individual tool or function, like a credit check agent or a refund agent. They are the building blocks of agentic AI. And, agentic AI is the comprehensive system that employs, coordinates, monitors and corrects multiple AI agents to collaboratively achieve a high-level goal.

How does agentic AI work?

Agentic AI works through an iterative decision-making loop that allows a system to pursue goals autonomously, take action and continuously refine its approach based on outcomes. Rather than responding once to a prompt, an agent observes its environment, decides what to do next, executes actions and evaluates results, repeating this cycle until the objective is achieved.

This loop is typically implemented through five core components that enable autonomy and self-correction:

1. Perception: Agentic AI begins gathering information from the environment (e.g., reads email, checks a database, monitors a queue).
2. Reasoning: The LLM “brain” analyses the current state and determines the next best step toward the primary goal.
3. Planning: Breaks down the high-level goal into an executable, multi-step sequence (a “plan”) and selects the appropriate tools.
4. Action: Executes the planned step by calling external tools, APIs or initiating workflows (e.g., sending an API request, initiating an RPA bot).
5. Reflection: Evaluates the results of the action, learns from success or failure and updates the internal plan for future steps.

Agentic AI features

The core components listed above translate directly into high-value features for your organisation:

1. Proactive goal-setting: Agents don’t wait for a prompt; they actively seek to fulfil objectives like maintaining optimal inventory levels.
2. Multi-step reasoning: Ability to connect and orchestrate actions across disparate systems like CRM, ERP and RPA to achieve complex outcomes.
3. Adaptive workflows: The Reflection step ensures processes automatically adjust to changing conditions or data, eliminating the need for constant manual re-engineering.
4. Enhanced auditability: Enterprise-grade platforms provide immutable audit trails that log every Perception, Plan, and Action for compliance and explainability.

Many organisations tout agentic capabilities, yet often these are limited to basic automation or scripted responses. True agentic AI involves a higher level of autonomy and adaptability, characteristics that are not easily achieved. It’s like the difference between a GPS that just gives directions versus one that reroutes you around traffic, suggests better routes based on your driving habits and learns your preferences over time.

What are the benefits of agentic AI?

The value of agentic AI lies in its ability to enhance workflows with flexibility and responsiveness. This is when the magic happens, when your systems become genuinely responsive rather than just reactive.

Unlike rigid rule-based systems, agentic AI can interpret natural language, make context-aware decisions and access a wide range of predefined tools. Imagine workflows that understand context, interpret what people actually mean (not literal explanations) and tap into your existing tools seamlessly. All this is within the Liberty platform. While some setup is required (it isn’t real magic), the design enables broader coverage of edge cases without mapping out every scenario in advance.

This translates to some compelling advantages:

1. Faster deployment
2. Natural conversations – teams don’t have to learn special commands or navigate complex interfaces
3. Lower change overheads
4. Smarter decision-making – as context is considered
5. Flexibility – when faced with something unexpected, it finds a solution with available resources
6. People are kept central to the process, while inefficiencies are removed via intelligent automation.

Netcall’s distinctive approach to agentic AI

At Netcall, we recognise the transformative potential of genuine agentic AI. Our Liberty platform integrates a suite of tools designed to deliver authentic agentic functionalities:

1. Liberty Create: Empowers users to develop applications that can adapt to changing requirements without extensive coding knowledge
2. Liberty Converse: Enhances customer engagement through intelligent routing and context-aware interactions
3. Liberty RPA: Automates repetitive tasks while allowing for dynamic decision-making processes.

“Our extensive experience across sectors, including local government, healthcare, retail, finance and insurance, positions us uniquely to implement agentic AI solutions which are both effective and compliant with industry standards.”

Chris Martin

Product Owner – Liberty AI, Netcall

Agentic AI use cases

Let’s look at some scenarios across various sectors, which illustrate how AI agents autonomously select and execute functions, streamlining complex workflows and enhancing efficiency.

Agentic AI considerations and best practices

Risks and challenges

It’s also important to be mindful of the potential risks that come with this independent system.

1. Error propagation: A single mistake (hallucination, misclassification) by one agent can cascade across interconnected systems and other agents, amplifying the failure (chained vulnerabilities).
2. Unbounded execution: Recursive planning loops can cause an agent to consume massive computing resources, leading to runaway costs and system disruptions.
3. Transparency and explainability: Due to the complexity of the underlying LLMs and the dynamic nature of the planning cycle, it can be challenging to explain why an agent chose a specific course of action over another, hindering internal compliance and external regulatory review.
4. Privacy and data handling: Agents handle and process sensitive customer and organisational data across multiple endpoints during their operation. This raises significant concerns about adherence to regulations such as GDPR or HIPAA, especially regarding the management of data provenance and deletion requests across the entire execution chain.

Best practices

1. Clear objectives: Identify the specific problems that AI agents will solve and how they align with business goals.
2. Data provenance and deletion: Implement platform-level controls to track where data is used and stored throughout the agent’s workflow, enabling efficient compliance with privacy mandates such as data erasure requests.
3. Security: Agentic AI systems are susceptible to cyber threats. Companies must deploy strong security protocols to safeguard these systems and their data. This involves preventing data breaches, unauthorised access, and malicious activities that could endanger the system’s integrity or confidentiality.
4. Integration: Merging agentic AI systems with current business infrastructure is a complex task that demands meticulous planning and coordination. It requires ensuring compatibility with existing IT setups, data formats, and operational workflows. Challenges such as data migration, system integration, and user acceptance must also be addressed.

The future of agentic AI

Agentic AI has moved past the experimentation phase. In 2024, the global market was valued at $5.1 billion. That figure is projected to exceed $47 billion, reflecting rapid agentic AI growth at a compound annual rate of more than 44%.

With the current state of automation, productivity and genAI foundation, agentic AI is moving beyond pilot programs to impact the business nature of work.

Balancing technical excellence with accessibility

While our enterprise AI platform solutions are rooted in advanced technology, we prioritise user-friendly interfaces and clear communication. By demystifying agentic AI, we aim to make its benefits accessible to a broader audience, ensuring that stakeholders – from technical teams to investors – understand its value proposition.

As agentic AI continues to evolve, distinguishing between superficial implementations and genuine capabilities becomes increasingly important. Netcall remains committed to delivering authentic, effective agentic AI solutions, leveraging our robust platform and cross-industry expertise to drive meaningful innovation.

About the author

Chris Martin

Product Owner (Liberty AI)

Chris serves as the link between artificial intelligence development and product at Netcall, bringing AI features to life. After studying Machine Learning at university, he launched his career as an ML engineer at international firms, working closely with executives and senior teams. That experience naturally led him into product management, where he has also undergone formal training. Today, Chris combines his technical background with a product-focused mindset to drive Netcall's AI roadmap and ensure its solutions deliver real value.