Introduction

Agentic AI refers to a new class of artificial intelligence systems capable of executing multistep tasks, adapting to processes, and performing actions independently rather than merely responding to prompts. The term has witnessed a rapid surge in public and industry attention, driven by new academic reports and its promise of automating complex workflows. The development marks a notable shift from conventional chatbots that were largely conversational and instruction-bound.

Why in the News?

It is in the news due to a new report by the Massachusetts Institute of Technology and the Boston Consulting Group describing it as a “new class of systems that can plan, act, and learn on their own.” Google searches for the term have skyrocketed, reflecting a sharp contrast from its obscurity just a year ago.

What Makes Agentic AI Different?

1. Autonomous Execution: Moves beyond responding to instructions by executing multistep processes and adapting as they proceed.
2. Planning Capability: Breaks high-level goals into sequential steps and performs them independently.
3. Human-Like Behaviour: Sounds more natural and expressive, yet retains training-based limitations without genuine understanding.

Why Has the Term Skyrocketed?

1. New MIT–BCG Report: Classifies agentic systems as a new AI class with independence in planning and learning.
2. Search Spike: Google searches for the term hit a peak earlier this fall.
3. Corporate Adoption: Major tech firms such as OpenAI, Google, IBM, Microsoft, and Salesforce are building or integrating agentic systems.

How Does Agentic AI Work in Real-world Tasks?

1. Execution of Goal Chains: Systems take inputs like “Here are the great ideas” and “And then complete the task.”
2. Application in Online Services: Includes personal finance assistance, bill interpretation, dispute resolution, or travel booking using card data.
3. Complex Task Automation: Involves computer access and stepwise execution of guidelines for high-level objectives.

What Is Driving Industry Optimism?

1. Workflow Automation Promise: Amazon sees agentic systems as key to automating cloud operations and enterprise-level tasks.
2. Operational Transformation: Viewed as one of the biggest AI evolutions since early generative models.
3. Security Applications: Potential as “personal shields” against spam, fraud, and phishing by acting on email and digital data.

What Are The Concerns or Limitations?

1. Marketing Hype vs Utility: The term is being debated due to its sudden popularity and vague boundaries.
2. Lack of True Autonomy: Systems act within training limits despite appearing highly capable.
3. Ethical and Trust Issues: The blending of autonomous actions with sensitive tasks (finance/computers) raises oversight concerns.

Conclusion

Agentic AI represents a shift from conversational to autonomous process-executing systems. While the term has rapidly gained traction due to academic endorsement and industry optimism, its real potential depends on responsible deployment, ethical guardrails, and clarity around autonomy and control. Its emergence signals an important moment in the evolution of artificial intelligence with direct implications for governance, security, and digital administration.

PYQ Relevance

[UPSC 2023] How can Artificial Intelligence (AI) help clinical diagnosis? Do you perceive any threat to privacy of the individual in the use of AI in healthcare?

Linkage: Agentic AI builds on this by not just assisting but autonomously executing tasks such as interpreting bills or acting on sensitive data. The privacy risks highlighted in the PYQ directly connect to concerns over AI agents accessing personal digital information while acting independently.

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