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The Rise of Agentic Systems: From Bots To Agents

Jayesh Govindarajan

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Agentic AI systems FAQs

Agentic AI systems are advanced artificial intelligence frameworks that allow AI models to autonomously understand goals, break them into sub-tasks, reason, plan, and execute actions in a dynamic environment.

They function through continuous loops of perception (observing data), reasoning (processing information and making decisions), planning (forming action sequences), and execution (performing actions).

Key components of agentic AI systems include a large language model (LLM) for reasoning, tools and APIs for taking actions, and mechanisms for planning and decision-making.

Benefits include increased automation of complex workflows, enhanced decision-making capabilities, greater efficiency, and the ability to adapt to changing conditions without constant human oversight.

Applications span customer service automation, sales enablement, supply chain management, data analysis, content creation, and personalized marketing campaigns.

They learn through feedback loops from their actions, reinforcement learning, and by continually processing new data, allowing them to refine their strategies and performance.

Ethical concerns include ensuring transparency in decision-making, addressing potential biases in autonomous actions, maintaining data privacy, and establishing accountability frameworks.