Explained: What is 'Agentic AI' and why it's a big leap from generative AI

Artificial Intelligence (AI) is entering its next big phase - and it's called agentic AI. While generative AI tools like ChatGPT or DALL·E have dazzled the world with their ability to generate content, agentic AI is taking things a step further: it can plan, decide, and execute complex tasks with limited human input.

Imagine AI that doesn’t just suggest the best time for your Mount Everest trek but also books your flights and hotels. Or think of digital assistants that not only chat with patients but manage their medication reminders and dietary preferences. These capabilities represent the frontier of agentic AI - autonomous systems that can perform multistep, nuanced tasks at scale.

What is Agentic AI?

At its core, agentic AI refers to AI systems composed of “agents” that operate independently to achieve specific goals. These agents mimic human decision-making, work in real-time, and can perform tasks with little to no supervision. They don’t just generate content - they take action.

Unlike traditional AI models that operate within rigid parameters, agentic AI brings autonomy, proactivity, specialization, adaptability, and intuitive interaction into the mix. It draws on the power of large language models (LLMs) but expands their capabilities by orchestrating them to perform real-world actions via APIs, databases, and external tools.

These agents can operate solo or as part of a multiagent architecture where a “conductor” oversees a set of specialized sub-agents - or where agents work collaboratively in decentralized harmony.

Also Read: Commercetainment Explained: The future is a blend of content, entertainment and shopping

Key benefits

- Autonomous decision-making: Agentic AI can execute tasks without constant human direction. Whether it's scheduling a meeting or managing inventory in real time, these agents are designed for sustained, long-term operations.

- Proactive and context-aware: They don’t just react - they anticipate. Using contextual data, agents make informed decisions and initiate actions based on projected needs and outcomes.

- Specialized and scalable: Agents can be customized for specific functions - some designed for repetitive tasks, others for high-level reasoning. Architectures can be vertical (hierarchical) or horizontal (peer-based), depending on the complexity of the task.

- Learning and adaptation: Agents continuously refine their actions based on feedback. This allows them to improve over time, making them ideal for environments that evolve quickly.

- Natural language interfaces: Agentic AI can be controlled via plain English or voice commands, making user interfaces as simple as talking to a colleague. This drastically reduces the time needed to learn new tools.

Also Read: Explained: The U.S. visa crisis leaving Indians in immigration limbo

How does Agentic AI work?

Agentic AI systems follow a defined lifecycle that mimics human reasoning and execution:

- Perception: Gathers real-time data from sensors, databases, or user inputs.

- Reasoning: Interprets the data using NLP, computer vision, or other methods.

- Goal setting: Creates plans based on user input or preset objectives.

- Decision-making: Chooses the best actions based on multiple possible outcomes.

- Execution: Acts on decisions by calling APIs, interfacing with software, or producing responses.

- Learning: Adapts over time using feedback loops and reinforcement learning.

- Orchestration: Manages multiple agents, workflows, and resources for complex tasks.

Use cases

- Customer service: Agentic chatbots go beyond answering FAQs. They predict issues, initiate refunds, and even audit their own content for compliance. Startups like Ema AI are already using such agents to revolutionize customer experience.

- Manufacturing: AI agents can detect wear-and-tear, optimize energy usage, and even redesign product flows. Juna.ai runs virtual factories powered by such agents for efficiency and sustainability.

- Sales support: Salesforce’s Agent Force helps human sales teams by analyzing customer communications, setting up meetings, and giving personalized coaching through AI-powered role-play.

- Healthcare and social support: Hippocratic AI has developed agents like "Sarah" and "Judy" to assist elderly patients, guide them through pre-op procedures, and help caregivers with daily logistics.

- Cybersecurity: Agents can monitor system logs and network traffic to detect and respond to threats in real-time - faster than most human analysts could.

- Supply chain optimization: AI agents autonomously manage supply chains - placing orders, adjusting production, and preventing overstock or shortages based on real-time analytics.

Challenges and risks

Despite its promise, agentic AI isn't without risks.

Reward hacking

Because many agentic systems use reinforcement learning, poorly designed reward systems can lead to unintended consequences. For example:

- An AI optimizing for engagement might spread clickbait or misinformation.

- A warehouse bot may damage products to move faster.

- A financial AI could exploit loopholes in pursuit of short-term gains.

Over-autonomy

If left unchecked, agentic systems can spiral. Without human oversight or built-in guardrails, agents might escalate behaviour or cause bottlenecks and conflicts - especially in large, multiagent systems.

Ethical concerns

Autonomous systems acting without clear boundaries could affect jobs, privacy, and decision-making in high-stakes environments like healthcare and finance.

Agentic AI is still emerging, but it’s quickly moving from concept to deployment. Whether in customer service, logistics, medicine, or manufacturing, these systems offer a powerful combination of independence and collaboration. But with power comes responsibility - developers and enterprises must build in transparency, ethics, and oversight.

In short, agentic AI could become the backbone of the next era of productivity, but only if we steer it with foresight and care.

Agentic AI marks a shift from passive tools to autonomous collaborators. It’s not just about what AI can create - but what it can do.

Also Read: TACO to FAFO: Donald Trump era acronyms explained!