Recently, major cloud platforms have been promoting Agentic AI through various events, drawing parallels to past tech trends like blockchain and ML that ultimately fell flat for many startups. Agentic AI claims to be mostly autonomous, goal-oriented, capable of planning and reasoning, and able to learn from feedback. However, practical experiences show a different reality. Senior software engineers often find these systems require significant guidance and structure, leading to messy outputs. Research indicates that LLMs lose coherence quickly in multi-turn interactions and struggle with complex reasoning. Despite this, tools like Copilot are widely adopted, signaling some utility. The author argues for a paradigm shift, suggesting that instead of aiming for reduced human involvement, AI systems should enhance human decision-making. The proposed approach involves prioritizing human approval in decision-making, anticipating system degradation, and measuring success based on improvements in human productivity rather than autonomy.
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