AtumAI: A Principled Framework for Agentic Generation of Datacenter Control-Plane Policies
- URL: http://arxiv.org/abs/2608.02569v1
- Date: Mon, 03 Aug 2026 17:45:58 GMT
- Title: AtumAI: A Principled Framework for Agentic Generation of Datacenter Control-Plane Policies
- Abstract summary: We introduce AtumAI, a framework that generates control-plane policies with agentic AI.<n>AtumAI autonomously proposes, tests, and refines candidate policies until one satisfies the request.<n>It consistently outperforms expert-engineered baselines on three control-plane tasks.
- Score: 9.93200224309013
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: The efficiency of a datacenter rests on its control plane policies. Designing these policies is increasingly hard: the hardware-software stack grows fast, the design space is vast and interdependent, and prototyping a single policy takes months. Agentic AI promises to automate this search. Off the shelf, however, it falls short on three fronts. It is not formal: with no structured, searchable statement of the problem, the search has little structure to exploit and hard constraints are not guaranteed. It is not transferable: each task is solved from scratch, so nothing learned on one task carries to the next. Finally, it is not systematic: relying on the LLM as the sole source of candidates, it explores a narrow slice of the design space and settles into local optima. We introduce AtumAI, a framework that generates datacenter control-plane policies with agentic AI, making the process formal, transferable, and systematic. From a goal stated in plain language, AtumAI autonomously proposes, tests, and refines candidate policies until one satisfies the request. It does so through two components. The Datacenter Task Compiler automates problem formulation: it compiles the request into a formal, machine-checkable, and searchable specification of the task's objectives, constraints, decision variables, and evaluation methodology. The Evolutionary Design Discovery Loop then searches this specification, expanding the search beyond the LLM itself via a diffusion model, an evolutionary algorithm, and a surrogate model. Together, they reduce onboarding a new task from months of engineering to writing its description. We evaluate AtumAI on three control-plane tasks with distinct problem scopes, design spaces, and trade-offs: workload placement, resource scaling, and power management. Across all tasks, the policies generated by AtumAI consistently outperform expert-engineered baselines.
Related papers
- Do AI Agents Know When a Task Is Simple? Toward Complexity-Aware Reasoning and Execution [1.733151600403503]
Large language model (LLM) agents increasingly automate multi-step engineering and informatics.<n>They often follow a maximum-context-first strategy--re-reading files and dependencies they have already seen--turning a one-line edit into a small code-base audit.<n>We argue the missing capability is task-aware execution-scope estimation.
arXiv Detail & Related papers (2026-07-14T17:59:31Z) - Spatial Atlas: Compute-Grounded Reasoning for Spatial-Aware Research Agent Benchmarks [1.5229257192293202]
We introduce compute-grounded reasoning (CGR) for spatial-aware research agents.<n>CGR is a design paradigm for spatial-aware research agents in which every answerable sub-problem is resolved by deterministic computation.<n>We show that CGR yields competitive accuracy while maintaining interpretability.
arXiv Detail & Related papers (2026-04-13T22:22:07Z) - Advances and Frontiers of LLM-based Issue Resolution in Software Engineering: A Comprehensive Survey [59.3507264893654]
Issue resolution is a complex Software Engineering task integral to real-world development.<n> benchmarks like SWE-bench revealed this task as profoundly difficult for large language models.<n>This paper presents a systematic survey of this emerging domain.
arXiv Detail & Related papers (2026-01-15T18:55:03Z) - A Comprehensive Survey on Benchmarks and Solutions in Software Engineering of LLM-Empowered Agentic System [56.40989626804489]
This survey provides the first holistic analysis of Large Language Models-powered software engineering.<n>We review over 150 recent papers and propose a taxonomy along two key dimensions: (1) Solutions, categorized into prompt-based, fine-tuning-based, and agent-based paradigms, and (2) Benchmarks, including tasks such as code generation, translation, and repair.
arXiv Detail & Related papers (2025-10-10T06:56:50Z) - Barbarians at the Gate: How AI is Upending Systems Research [58.95406995634148]
We argue that systems research, long focused on designing and evaluating new performance-oriented algorithms, is particularly well-suited for AI-driven solution discovery.<n>We term this approach as AI-Driven Research for Systems ( ADRS), which iteratively generates, evaluates, and refines solutions.<n>Our results highlight both the disruptive potential and the urgent need to adapt systems research practices in the age of AI.
arXiv Detail & Related papers (2025-10-07T17:49:24Z) - The Foundations of Computational Management: A Systematic Approach to
Task Automation for the Integration of Artificial Intelligence into Existing
Workflows [55.2480439325792]
This article introduces Computational Management, a systematic approach to task automation.
The article offers three easy step-by-step procedures to begin the process of implementing AI within a workflow.
arXiv Detail & Related papers (2024-02-07T01:45:14Z) - Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning [50.47568731994238]
Key method for creating Artificial Intelligence (AI) agents is Reinforcement Learning (RL)
This paper presents a general framework model for integrating and learning structured reasoning into AI agents' policies.
arXiv Detail & Related papers (2023-12-22T17:57:57Z) - Plan, Eliminate, and Track -- Language Models are Good Teachers for
Embodied Agents [99.17668730578586]
Pre-trained large language models (LLMs) capture procedural knowledge about the world.
Plan, Eliminate, and Track (PET) framework translates a task description into a list of high-level sub-tasks.
PET framework leads to a significant 15% improvement over SOTA for generalization to human goal specifications.
arXiv Detail & Related papers (2023-05-03T20:11:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.