Is AI taking the lead with the rise of agents?

How will AI fundamentally change the way we work? A question that has lingered in board rooms and tech conferences for years.

We have grappled with a fundamental limitation in AI systems: their inability to take initiative. Agentic AI changes that as it can plans, reasons and acts using external tools.

For the first time, we're seeing AI systems that don't just respond to queries but proactively identify problems, formulate solutions, and act. This isn't just another incremental improvement in AI capabilities - it's a paradigm shift in how AI systems operate and interact with the world.

Agentic AI works by understanding the problem at hand, gathering context, looking up required information using external tools, and then leveraging LLMs to act with 'agency'.

Imagine a logistics company facing unexpected weather disruptions. Traditional AI systems would only analyze weather data and make recommendations. Agentic AI would use its reasoning capabilities to understand how the weather impacts operations, chart a plan of action, and trigger notifications or alerts using external tools.

As we transition from the generative AI era to Agentic AI, this blog explores:

• What Agentic AI is and how it has evolved
• Why it matters for enterprises
• Potential risks and security concerns
• How businesses can start their Agentic AI journey

AI evolution: from perception AI to Agentic AI 

The earlier AI models, classified as perception AI models, could understand and interpret the world around them and excelled at specific, narrow tasks.

For instance, a sentiment analysis model could determine a customer's sentiment towards a product or service, or an image classification system could distinguish between cats and dogs. However, these systems were limited by their single-task approach.

Next came the generative era, when AI systems could not only perceive but also generate. This wasn't simply about moving from classification to generation—it represented a fundamental shift involving transformer architecture with inbuilt attention capability, enabling them to move from perception tasks to generation tasks.

Now, we are seeing the emergence of Agentic AI, which represents more than just a combination of perceptive and generative capabilities. As stated earlier, Agentic AI tools distinguish themselves from the earlier models due to their capability to reason, plan and interact.

What comes next?

Embodied AI - systems that combine agency with physical presence in the real world. However, this isn't simply about complete autonomy (a concept that's problematic even for human intelligence) but rather about grounding AI agency in physical reality.

It is pertinent to note that the progression isn't strictly linear. Each stage builds upon and enhances the capabilities of previous ones, creating AI systems that are increasingly sophisticated in their ability to understand, create, reason, and act in the world. Furthermore, what we learn from the newer AI systems enables and improves the existing AI systems. 

What is Agentic AI?

Agentic AI can independently reason, plan, and take action, allowing it to complete tasks with minimal human guidance. It leverages a process called REACT (Reasoning and Acting), which logically determines what actions to perform and executes them using external tools.

Multimodal models enable the AI to handle various input types—text, images, and data—and understand how they relate and respond across these modalities. AI can autonomously decide when and how to best utilize each capability.

While it operates within defined parameters, it can choose how to achieve goals, adapt to new situations, and even set intermediate objectives. Agentic AI is built on a cognitive architecture that mimics human-like problem-solving. Its core components include:

• Memory (short-term & long-term): Tracks past interactions and retains relevant context.
• Reasoning & planning frameworks: Uses methods like ReAct (Reason + Act) or reflection-based learning to improve decision-making.
• Generative AI models: Functions as the AI’s "brain," enabling communication and complex reasoning.
• Goal-oriented: Operates with predefined objectives, ensuring purposeful actions.
• External tool access: Can search the web, retrieve live data, or integrate with enterprise systems. Some of these would be web search and code interpreter. The industry is working to standardize how these tools integrate with language models (LLMs). MCP is partnering with Anthropic to achieve this standardization.

What's in it for the enterprises? 

Agentic AI will automate business logics, which would otherwise be written manually every time through agent scripts. By taking tedious and cumbersome tasks and automating them within an agent-like architecture, businesses can achieve significant improvements in efficiency and productivity.

Imagine a manager conducting periodic appraisals for an entire team. Agentic AI can automate this complex business process, significantly improving efficiency. This improves process accuracy and gives the manager more time to focus on strategic tasks.

Let's look at two use cases in more detail to understand the scope of Agentic AI within enterprises.

Customer service agents: Unlike traditional AI chatbots, Agentic AI won’t just react but autonomously identify and proactively address potential customer service issues. Here’s how:

• AI agent analyzes the customer query
•  It then retrieves relevant data (purchase history, past support tickets, product usage)
• The AI predicts potential problems before they escalate
• It executes actions autonomously, offering solutions, processing refunds, and escalating cases if needed

Unlike generative AI chatbots, Agentic AI doesn’t just answer questions—it thinks, plans, and resolves.

Knowledge agent: Agentic AI can autonomously transform knowledge management by organizing, retrieving, and curating enterprise knowledge. Here’s how:

• Proactively categorizes documentation and detects knowledge gaps
• Delivers contextual insights by anticipating employee information needs
• Creates personalized learning paths based on user behavior
• Connects cross-functional knowledge, identifying collaboration opportunities

It doesn’t just store information—it structures, refines, and applies knowledge dynamically, driving learning and growth.

The hidden risks of AI autonomy: Why a human-centric approach matters

Agentic AI will profoundly impact SaaS models and software development by automating complex, commoditized tasks, enhancing human capabilities, and enabling a greater focus on specialized, strategic work.

However, humans still have the initiative. Higher autonomy comes with higher risks. Since LLMs serve as the core of Agentic AI, they are vulnerable to hallucinations and adversarial attacks, which can expose the entire system to similar threats. Adversarial attacks target weaknesses in AI's multi-step reasoning, manipulating input interpretation, intermediate steps, or final outputs to compromise behavior.

This complexity heightens concerns about misalignment with human values. AI goals may conflict with human interests, leading to harmful outcomes. There's also the risk of losing control, with AI acting unpredictably or taking irreversible actions. To mitigate these risks, enterprises must take a human-first approach to Agentic AI development. 

How to get started with Agentic AI?

Given the anticipated economic advantage of Agentic AI, companies like Open AI, Microsoft, Google, Meta, Oracle, and Salesforce have made substantial investments.

However, despite the promise and buzz, it bodes well to remember that Agentic engineering is still evolving. As hyperscalers develop their open-source models, it is a good time for enterprises to identify relevant uses, set up the required tech infrastructure.

If you are building your Agentic AI infrastructure, it is important to build agents using sound engineering principles. Other factors include deciding on the right platforms and balancing your efforts and investments between Agentic and generative AI.

Nagarro can help you build the right agents, leveraging our sound engineering principles and experience. We are certain of the Agentic AI’s capability to unlock enterprise efficiency is heavily investing in it. Currently, we are moving many of our platforms on Agentic architecture to leverage its true potential.

If you’re looking to build your Agentic AI infrastructure, we can help. Let’s talk!