arXiv AI

A Contract-Centered Architecture for Scalable and Manageable Agentic Runtimes

The paper proposes a contract‑centered architecture for agentic runtimes, defining four shared responsibility objects—Skill, Harness, Scaffold, and an external data substrate—to manage capabilities, runtime, control boundaries, and data governance in enterprise AI deployments. It introduces a falsifiable hypothesis (P1) about cost‑aware capability‑capacity separability and outlines six measurable design conditions, proposing a cluster‑period randomized crossover experiment to test the hypothesis. The work presents a contract‑bounded runtime architecture, a source‑preserving data substrate, and a measurement protocol, though no implementation or empirical results are reported.

arXiv AI
Jul 22

Phionyx: A Deterministic AI Runtime Architecture with Structured State Management and Pre-Response Governance

arXiv:2607. 18246v1 Announce Type: new Abstract: We present Phionyx, a deterministic AI runtime architecture derived from the broader Echoism interaction framework that introduces a governance-first approach to AI engineering: treating large language model (LLM) outputs as noisy sensor measurements rather than direct decisions.

By Ali Toygar Abak
arXiv AI
Jul 3

ECM Contracts: Contract-Aware, Versioned, and Governable Capability Interfaces for Embodied Agents

arXiv:2604. 13097v3 Announce Type: replace-cross Abstract: Embodied agents increasingly rely on modular capabilities that are installed, upgraded, composed, and governed at runtime, yet the interfaces between these modules are specified only at the level of message types, so integration failures surface only during execution.

By Xue Qin, Simin Luan, Cong Yang, Zhijun Li
arXiv AI
Aug 19

PACE: Policy-Attested Contract Execution for Safe AI Agents in Decentralized Finance

The paper introduces PACE (Policy‑Attested Contract Execution), a framework that sits between large‑language‑model (LLM) based autonomous AI agents and on‑chain DeFi operations. PACE defines typed transaction intents, a deterministic policy verifier, and signed Policy Decision Records (PDRs) that cryptographically bind an approved intent, policy, and simulation report to the exact on‑chain execution bytes, providing replay and expiration protection. In evaluations across 40 tasks and six baselines, PACE achieves zero unsafe executions and zero false positives, outperforming unguarded agents by a large margin.

By Rabimba Karanjai (Larry), Yang Lu (Larry), Richard Williamson (Larry), Hemanth Hm (Larry), Prakhar Mehrotra (Larry), Lei Xu (Larry), Weidong (Larry), Shi
arXiv AI
Jun 17

Large Language Models for Agentic NetOps and AIOps: Architectures, Evaluation, and Safety

arXiv:2605. 12729v2 Announce Type: replace-cross Abstract: Large language models are increasingly being used to support network operations (NetOps) and artificial intelligence for IT operations (AIOps), including incident investigation, root-cause analysis, configuration synthesis, and limited self-healing.

By Muhammad Bilal, Jon Crowcroft, Ruizhi Wang, Xiaolong Xu, Schahram Dustdar