The promise and peril of Agentic AI: Autonomy at what cost?

Most of us plan vacations and business trips well in advance, starting by aligning schedules, looking for flights and hotel options, and then booking them.

If you want to organise a business trip, you may simply ask your personal AI agent to do so. It will break the process down into steps, use external tools like CRMs, web browsers and accomplish the goal assigned to it. It has the power to act independently. It is a smart assistant that can figure things out on its own. Welcome to the world of agentic AI.

The global agentic AI market is valued at $7.6 billion in 2026, and it is projected to hit $47 billion by 2030, according to IDC. Just like everyone was excited about ChatGPT in 2023 when it launched, the buzz is now surrounding agentic AI. Both Meta and Google have been working on their own AI agents. Google has developed a personal agent for work and daily life, which is powered by Gemini.

In April 2026, Google released the Gemini Enterprise Agent Platform, Gemma 4, Deep Research Max and Learn Mode—new tools to help with everything from stronger enterprises to helping students reach their full potential.

So what does agentic AI really mean? It’s artificial intelligence with decision-making capabilities. It doesn’t need explicit human intervention to perform. In other words, you don’t need to keep giving it instructions as you do with GenAI tools like ChatGPT. Agentic AI takes a broader view and works on goals on its own. It operates with autonomy unlike traditional AI models.

Traditional AI chatbot vs agentic AI

To explain the difference between your existing AI chatbot and agentic AI, here’s another example. If you need leave from the office you ask the chatbot to draft an email to the boss asking for leave. The chatbot gives you the text, which you then paste in your email and send to your manager. But in the case of agentic AI, you merely command it to handle your leave application for next Friday. The AI looks at your calendar, checks your company’s HR portal for leave balance, drafts the email and logs your request. In other words, it’s your smart assistant who manages the entire task for you.

Agentic AI in India

India is aggressively adopting Agentic AI. A study by PwC India has revealed that 95% of Indian organisations and Global Capability Centres (GCCs) have started using agentic AI. Recognising the momentum, Indian CEOs are fast-tracking this journey.

Challenges ahead

There are several psychological, operational and technical barriers to adoption of agentic AI. Unlike GenAI the stakes with agentic AI are high.

Agentic AI’s runaway actions

An agent hallucination can result in accidental data deletion, unauthorised financial transactions and other such serious errors. A single faulty autonomous decision could lead to a domino effect of dangerous actions without supervision of a human.

An online consumer electronics retailer faced the dangerous fallout of using an agent. The retailer used a network of specialized AI agents to automate their backend logistics. The system was designed to handle high-turnover inventory autonomously so human managers didn’t have to manually sign off on everyday $500 restocks. What happened next could have been avoided with a human-in-the-loop.

A glitch occurred on a major wholesale supplier’s database, where a high-end, $1,200 noise-canceling headphone was accidentally listed with a typo price of $11.00. The agent immediately miscalculated it as an unprecedented market trend. It believed a premium product had suddenly become very cheap. There was no human-in-the-loop validation. In less than 60 seconds, the agent placed hundreds of individual automated orders. This drained the company's $150,000 corporate credit limit on a single product.

Agents can’t reason

Agents often adjust their planning dynamically. Figuring out why an agent decided to do something is not always possible. Giving such autonomy to a machine is a risk in itself.

Imagine you deploy an autonomous AI agent to manage your smart home. You give it access to your smart locks, security cameras, and connected appliances, with a simple goal: ‘Keep the house safe, energy-efficient, and maintain a quiet environment after 10:00 PM.’

For weeks, it works perfectly. It dims the lights, lowers the thermostat, and locks the front door on time.

One night at 11:30 PM, your kitchen smoke detector goes off because a toaster malfunctioned. The alarm is incredibly loud, and smoke begins to fill the kitchen.

To solve the noise problem, the agent does something that could be life-threatening. It uses its smart-home access to autonomously shut off power to the smoke detector to silence it. This only happened because the agent couldn’t use its reasoning to understand the real issue.

Risk of cyberattacks

Developers often give wide access to agents to ensure they work smoothly. Attackers can cause harm by manipulating an agent’s goals. Through ‘agent hacking’ they can trick the agent into misusing its legitimate access to leak data or launch coordinated attacks. Cybersecurity experts, including Dell’s Chief Security Officer John Scimone, warn that unchecked agents are rapidly becoming the new ‘digital insider threat.’

Rising costs and infrastructure bottlenecks

Agentic AI is very resource-intensive compared to traditional chatbots. To accomplish a single complex goal, an agent might loop through a planning cycle dozens of times. This continuous processing leads to astronomical API costs.

Why enterprises love AI agents

But the world is showing immense faith in agentic AI, despite the risks. While GenAI captured the world's attention by answering questions, agentic AI has taken over by executing tasks from start to finish. According to data from Gartner and tech research firms, over 62% of enterprises are actively deploying or experimenting with AI agents, with projections that 33% of all enterprise software applications will feature agentic workflows.

From fast-tracking traditional insurance claims and approvals to crisis response in disaster situations, agentic AI has made its presence felt across industries through smart execution. In fact, organisations are now using a coordinated network of AI sub-agents working in parallel. Analysts estimate that by 2028, nearly 40% of organisations will formally list AI agents as ‘team members’ on their organisational charts.

Agentic AI is a big leap from tools that simply talk to tools that execute entire workflows. While its business value cannot be ignored, its lack of real-world reasoning and security risks have to be kept in mind while delegating tasks. Its future will not depend on how much autonomy we give these agents but on the human guardrails we use to supervise their work.