Andrej Karpathy explica por que as habilidades dos agentes de IA falham em fluxos de trabalho longos.

Agent skills in AI systems often struggle with reliability, especially in complex, multi-step workflows, due to their probabilistic nature leading to errors like hallucinations and skipped steps. These shortcomings pose significant risks in high-stakes fields such as medical diagnostics, regulatory compliance, and financial audits. Deterministic harness engineering emerges as a structured solution, employing frameworks that validate and gate outputs at each step, ensuring precision and consistency. Key features include state tracking, sub-agent delegation, context isolation, validation loops, and parallel processing. Real-world applications like Stripe’s minions and Anthropic’s plugins demonstrate how harnesses enhance scalability and error management. Harness engineering is evolving with advanced architectures aimed at improving reliability and efficiency further. This approach enables AI systems to meet stringent business demands by providing dependable and scalable automation for critical tasks. Moving beyond agent skills, harnesses represent the future of reliable AI workflow management in enterprise environments.

Fonte: https://www.geeky-gadgets.com/ai-agent-reliability/