We explore AI-driven distributed-systems policy design by combining stochastic code generation from large language models (LLMs) with deterministic verification in a domain-specific simulator. Using a Function-as-a-Service runtime (Bauplan) and its open-source simulator (Eudoxia) as a case study, we frame scheduler design as an iterative generate-and-verify loop: an LLM proposes a Python policy, the simulator evaluates it on standardized traces, and structured feedback steers subsequent generations. This setup preserves interpretability while enabling targeted search over a large design space. We detail the system architecture and report preliminary results on throughput improvements across multiple models. Beyond early gains, we discuss the limits of the current setup and outline next steps; in particular, we conjecture that AI will be crucial for scaling this methodology by helping to bootstrap new simulators.

Starting from this prototype, we conclude by outlining practical next steps for a full agentic lakehouse. The paper is organized as follows. After reviewing agent-friendly abstractions (Section II), we address key safety objections for high-stakes scenarios (Section III). Once safety is established, we describe a ReAct [12] loop built on these abstractions (Section IV). We put forward our working prototype as a feasibility demonstration of safe-by-design data agents, not as a full-fledged experimental benchmark. We believe that sharing working code is of great value to the community, especially in times of quickly shifting mental models. However, it is important to remember that our fundamental insights - programmability and safety - can be replicated independently of the chosen APIs. For these reasons, we believe our paper to be valuable to a wide range of practitioners: on one hand, those looking for a new mental map of this uncharted territory; on the other, those looking to be inspired by tinkering with existing implementations and inspecting systems working at scale.

In this light, data workloads seem to Chaining functions for longer workloads is a key use case for FaaS be a natural fit for Function-as-a-Service (FaaS) platforms designed platforms in data applications. However, modern data pipelines to efficiently handle bursty, functional, and event-driven tasks. Unfortunately, differ significantly from typical serverless use cases (e.g., webhooks existing FaaS runtimes fall short in practice as they and microservices); this makes it difficult to retrofit existing pipeline were primarily designed to support the execution of many simple, frameworks due to structural constraints. In this paper, we describe independent functions that produce small outputs. Although popular these limitations in detail and introduce bauplan, a novel FaaS FaaS platforms (e.g., AWS Lambda [5], Azure Functions [17], and programming model and serverless runtime designed for data practitioners. OpenWhisk [4]) have added support for function chaining, their bauplan enables users to declaratively define functional capabilities fall short for data pipelines. It is therefore not surprising Directed Acyclic Graphs (DAGs) along with their runtime environments, that widely used data engineering frameworks (e.g., Airflow [1], which are then efficiently executed on cloud-based workers. Prefect [19], and Luigi [23]) lack native integration with serverless We show that bauplan achieves both better performance and a runtimes.

We introduce a method that permits to co-evolve the body and the control properties of robots. It can be used to adapt the morphological traits of robots with a hand-designed morphological bauplan or to evolve the morphological bauplan as well. Our results indicate that robots with co-adapted body and control traits outperform robots with fixed hand-designed morphologies. Interestingly, the advantage is not due to the selection of better morphologies but rather to the mutual scaffolding process that results from the possibility to co-adapt the morphological traits to the control traits and vice versa. Our results also demonstrate that morphological variations do not necessarily have destructive effects on robot skills.