arXiv AI
ECAS: An Edge-Controlled Agentic System for Validation-Gated Scientific Application Execution
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arXiv AI
1d ago
Don't Prompt Your Agents. Equip Them: Skillware as a Modular Standard for Sovereign, Auditable, and Deterministic Capability Execution for Logical Systems
The paper introduces Skillware, an open architectural framework that separates an agent’s intent from its execution by providing modular, versioned software capability bundles. These bundles include typed JSON contracts, deterministic Python code, task directives, datasets, and unit tests, allowing agents to perform tasks reliably without relying on fragile prompt engineering. Experiments across 23 models and 368 benchmarks show that small local models equipped with Skillware outperform larger frontier models, reducing token usage by up to 84.8%, cutting inference costs by up to 97.3%, and eliminating parameter errors.
arXiv AI
Sep 2
Towards Agentic Cloud Engineering: Graph and Loop Engineering with a Zero-Trust Agent Harness
The paper introduces Agentic Cloud Workflow Engineering, a framework that converts natural‑language agentic cloud‑engineering tasks into validated code repositories and verified cloud deployments. It separates graph engineering for long‑horizon workflow progression, loop engineering for bounded diagnosis and recovery, and agent harness engineering for zero‑trust execution. Experiments on Google Cloud show that executions either produce a verified deployment or an auditable terminal failure within bounded recovery limits.
arXiv AI
Sep 30
ASCEND: Personal AI Agents for Autonomous Scientific Computing Across HPC Clusters and GPU Workstations
arXiv:2609.32868v2 Announce Type: replace-cross Abstract: Traditional scientific computing requires researchers to translate computational intent into environment configuration, resource requests, an...
arXiv AI
Jun 9
Semantic Quorum Assurance: Collective Certification for Non-Deterministic AI Infrastructure
arXiv:2606. 08021v1 Announce Type: cross Abstract: As large language model (LLM) agents are integrated into autonomous cloud operations, distributed systems face a semantic reliability problem: proposer agents can generate production mutations, such as modifying IAM policies, opening firewall security groups, or executing data exports, that are syntactically valid and statically authorized but operationally unsafe.
arXiv AI
Aug 19
SkillEffect: Checked Lowering for Memory-Bounded Agent Tools
SkillEffect is a checked‑lowering runtime that ensures agent tool calls stay within memory limits by verifying each proposed program against an immutable input before execution. It uses audited relation plugins to provide source recognition, bounded intermediate representation construction, and postconditions, while a shared runtime handles selection, bounded VM execution, and atomic capacity leasing. Experiments across six operator families show that bounded access significantly reduces peak memory usage and improves completion rates under fixed memory caps.
arXiv AI
Jul 21
AEVAL: From Anecdotal to Deterministic Testing for Agentic Skill Workflows
arXiv:2607. 16345v1 Announce Type: cross Abstract: Modern agentic systems increasingly rely on skills: installable packages of natural language and code that teach an LLM agent to perform a domain task.