INTRODUCTION TO AGENTIC INTELLIGENCE
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Abstract
Agentic intelligence represents a paradigm shift in the evolution of artificial intelligence, transitioning AI systems from passive, reactive tools to autonomous agents capable of perceiving complex environments, formulating multi-step plans, executing goal-directed actions, and continuously learning from outcomes with minimal human intervention. Unlike conventional AI models that respond to singular, well-defined prompts, agentic systems integrate perception, memory, reasoning, planning, and actuation into a cohesive cognitive loop that mirrors the functional architecture of human agency. The emergence of large language model-based agents, reinforcement-learning-driven autonomous controllers, and multi-agent collaborative frameworks has accelerated the deployment of agentic intelligence across domains as diverse as healthcare diagnostics, autonomous mobility, financial decision-making, industrial automation, scientific discovery, and national cybersecurity infrastructure. Contemporary agentic architectures leverage transformer-based foundation models augmented with tool-use capabilities, long-context memory modules, chain-of-thought reasoning chains, and real-time feedback mechanisms to achieve tasks that were computationally intractable just a decade ago. Empirical benchmarks demonstrate that state-of-the-art agentic systems achieve task completion rates exceeding 90% on complex, long-horizon benchmarks such as WebArena, SWE-bench, and AgentBench, compared to below 55% for traditional single-pass AI models under equivalent conditions. However, the ascent of agentic intelligence also surfaces profound technical, ethical, and governance challenges—including alignment failures, emergent deceptive behaviour, adversarial vulnerabilities, opaque decision chains, and the concentration of autonomous power within AI systems operating at speeds and scales beyond direct human oversight.
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