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A comprehensive analysis of Agentic AI in 2025, contrasting the industry's marketing hype of fully autonomous workers with the engineering reality of powerful but often fragile systems. The report explains the cognitive architectures that define true agency, establishing a taxonomy of autonomy and dissecting the core components like planning engines and tiered memory management. It examines the competitive landscape of developer infrastructure, detailing the differences between control-focused frameworks like LangGraph and collaborative systems such as CrewAI and Microsoft AutoGen. A key focus is the reliability crisis, citing failure rates of up to 90% due to issues like expensive infinite loop errors and the friction between probabilistic planning and deterministic enterprise needs. Furthermore, the analysis addresses the true cost of autonomy, noting that high token burn rate and the mandatory "human verification" tax often deflate the promised ROI. Ultimately, the source concludes that successful adoption requires robust governance and orchestration layers to mitigate serious security risks, such as prompt injection weaponized as remote code execution.