Narada AI

If you’re using an agent for complex, multi-step tasks, you’d notice it’d quickly diverge to an irrelevant step after some steps. This is because LLMs do inferencing auto-regressively, and if each step is off by 1%, error accumulates to >200% after ~100 steps. Amir Gholami, research scientist at @berkeley_ai and co-founder of @Narada_AI, is fixing this by building the "x86 instruction set" for agents. It'd break down user request into small tasks to reason over, add exception handling, and use model as an “LPU” to catch errors early. Read about their agent framework in their paper, Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks Link.