Agentic Fault Recovery for Orchestration of Programmable Cloud Laboratories

Problem

Autonomous systems that orchestrate multi-step workflows must plan actions, respond to failures, and adapt as conditions change. Fixed scripts are often too rigid for this setting.

Approach

I am developing agentic systems that combine reinforcement learning with large language models for autonomous orchestration and fault recovery.

This work asks how an agent should detect that a protocol has deviated from plan, choose a safe recovery action, and resume without repeating completed physical operations. It connects simPCL, which enables controlled offline evaluation, with our MHS physical-laboratory work, which tests closed-loop operation on real equipment.

Status

Work in progress. Contributed talk at the AI Scientist Summer Workshop (Microsoft Research, Cambridge, MA, 2026): Agentic Fault Recovery for Autonomous Orchestration of Programmable Cloud Laboratories.