simPCL: An Open-Framework Simulator and Benchmark Generator for Programmable Cloud Laboratories

Problem

Evaluating autonomous agents for experimental orchestration on real laboratory hardware is slow and expensive. Progress on planning, fault recovery, closed-loop scientific discovery, and decision-making needs simulation environments and benchmarks that support controlled offline study.

Approach

I am building simPCL, an open-framework simulator and benchmark generator for programmable cloud laboratories. It supports controlled study of:

• long-horizon experimental planning;
• recovery from instrument and workflow failures;
• reliable execution across multi-step protocols; and
• closed-loop decisions in which measurements determine the next experiment.

simPCL complements our work on physical laboratory automation using MHS: real equipment demonstrates what an agent can accomplish, while simulation makes it possible to evaluate many scenarios reproducibly before using scarce laboratory time.

Status

Manuscript in preparation. Related work was presented at the AI Scientist Summer Workshop (Microsoft Research, 2026) in a contributed talk on agentic fault recovery. Code and benchmarks will be released with the paper.