Why in news?
Microsoft’s CEO Satya Nadella recently noted that India is witnessing “strong momentum” in the deployment of AI applications, including emerging agentic AI systems. This comment spurred discussions on what agentic AI entails and how it differs from earlier artificial intelligence approaches.
Background
Traditional AI systems perform tasks based on pre‑programmed rules or analyse data to recognise patterns. Generative AI models, such as large language models, can produce text, images or code by learning statistical patterns in data. Agentic AI goes a step further: it consists of AI agents that can set goals, plan and act autonomously to achieve a desired outcome. These agents combine perception, reasoning and generative capabilities to operate in dynamic environments with minimal human intervention.
Key components
- Perception: An agent gathers information from sensors, databases or user inputs to build a situational model.
- Reasoning: It analyses the information, identifies relevant patterns and infers relationships, often leveraging large language models or other machine‑learning techniques.
- Planning: The agent breaks down the overall goal into smaller steps, sequences actions and evaluates alternative strategies.
- Action: The agent executes tasks – such as sending messages, booking appointments or running software – possibly by invoking external tools.
- Reflection: After acting, the agent assesses the outcome, learns from feedback and adapts its future behaviour.
How it differs from generative AI
Generative AI specialises in creating content based on learned patterns; for example, ChatGPT can draft articles or code snippets. Agentic AI uses generative outputs as a means to an end: it can independently decide what content to produce, when to produce it and how to apply it to achieve specific goals. Instead of merely responding to prompts, an agentic system can initiate tasks, interact with multiple services and iterate until the desired objective is met.
Advantages and applications
- Autonomy: Agentic systems reduce the need for constant human supervision by handling complex workflows end‑to‑end.
- Proactive assistance: They can anticipate user needs and suggest or carry out actions, such as reminding users of deadlines or reordering supplies.
- Adaptability: By learning from interactions, agents can adjust their strategies to changing circumstances.
- Task orchestration: Agents can interface with calendars, email servers and other applications, making them useful for personal assistants, customer service bots and process automation.
Significance
Agentic AI promises to transform how people and organisations manage tasks. In India, where digital services are expanding rapidly, such systems could streamline administrative work, support farmers and small businesses with timely information, and improve access to healthcare and education. As with any AI technology, ethical considerations – transparency, accountability and fairness – must guide development and deployment.
Conclusion
Agentic AI represents an evolution in artificial intelligence, where machines move from passive responders to active decision‑makers. Understanding its capabilities and limits will help policymakers, engineers and users harness its benefits while mitigating risks.
Sources: TH
