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User notes that agentic AI systems fail in real-world use due to adversarial environments, compounding errors, and shifting state. They ask how advances in state tracking and recovery loops can be engineered to make these systems more robust and scalable beyond 'demo magic.'
๐ช๐ต๐ ๐ฎ๐ด๐ฒ๐ป๐๐ถ๐ฐ ๐๐ ๐น๐ผ๐ผ๐ธ๐ ๐ฎ๐บ๐ฎ๐๐ถ๐ป๐ด ๐ถ๐ป ๐ฑ๐ฒ๐บ๐ผ๐ โ ๐ฎ๐ป๐ฑ ๐ณ๐ฎ๐ถ๐น๐ ๐ถ๐ป ๐ฟ๐ฒ๐ฎ๐น ๐๐ผ๐ฟ๐น๐ฑ ๐๐๐ฒ This Stanford + Harvard paper nails a pattern many of us have been feeling intuitively. Most agentic AI systems donโt fail because the models are weak. They fail because the ๐๐๐ฃ๐๐๐๐๐๐๐๐ก they operate in is adversarial, incomplete, and constantly shifting. A few takeaways that really stood out: โข Agentic AI shines in clean, scripted demos but struggles with long-horizon tasks full of edge cases โข Small early mistakes compound rapidly, leading to total task collapse โข The hardest part isnโt reasoning - itโs state tracking, recovery, and coordination over time โข Evaluation benchmarks dramatically underestimate real-world brittleness This reinforces something important: Agentic AI isnโt a model problem. Itโs a systems problem. Success here will come from: โข Better task decomposition โข Stronger feedback loops and recovery mechanisms โข Explicit handling of uncertainty, not pretending it doesnโt exist Demos optimize for wow. Real products optimize for resilience. #AIrevolution #ArtificialIntelligence #LargeLanguageModels #GenerativeAI