LLM Agents

Fast or Slow? Human-Inspired Self-Evolving Framework for Resilient AI Systems

This paper proposes a disruptive shift toward human-like self-evolving loops as a foundation for resilient AI systems. At the core of our proposal is the PURER loop (Perceive, Update, Reason, Execute, Reflection), a cognitive- inspired framework that …

Sherlock: Reliable and Efficient Agentic Workflow Execution

With the increasing adoption of large language models (LLM), agentic workflows, which compose multiple LLM calls with tools, retrieval, and reasoning steps, are increasingly replacing traditional applications. However, such workflows are inherently …

Murakkab: Resource-Efficient Agentic Workflow Orchestration in Cloud Platforms

Agentic workflows commonly coordinate multiple models and tools with complex control logic. They are quickly becoming the dominant paradigm for AI applications. However, serving them remains inefficient with today's frameworks. The key problem is …