AGENTIC-SYSTEMSMay 28, 20261 min read13 views

Agyn: An Open-Source Platform for AI Agents with Scalable On-Demand Execution, Agent Definition as a Code, and Zero-Trust Access

As organizations transition AI agents to production, Agyn emerges as an open-source platform designed to address the challenges of scaling, governance, and security. Built on Kubernetes, Agyn leverages stateful serverless execution, infrastructure-as-code, and zero-trust security.

Computer Science > Artificial Intelligence

Title:Agyn: An Open-Source Platform for AI Agents with Scalable On-Demand Execution, Agent Definition as a Code, and Zero-Trust Access

View PDF HTML (experimental)Abstract:As organizations move toward production deployments of AI agents, which execute non-deterministic workflows, maintain stateful sessions, and often operate with privileged access to internal services, the engineering challenge shifts from building individual agents to operating them at scale with proper isolation, governance, and security. In this paper we present Agyn, an open-source platform designed around three key principles tailored for agent workloads: a signal-driven, stateful serverless runtime on Kubernetes; a Terraform provider for agent and harness definition; and a security model grounded in zero-trust and least-privilege principles. Agyn is agent-agnostic, model-agnostic, and cloud-agnostic.

Bibliographic and Citation Tools

Code, Data and Media Associated with this Article

Demos

Recommenders and Search Tools

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Source: arXiv cs.AI Recent

More in this category

agentic-systems

Capability from Access Structure, Not Scale: Lower Bounds and Pre-Registered Tests for Hybrid Sequence Models

A new study challenges the assumption that scaling alone guarantees capability convergence in AI models. Instead, researchers demonstrate that true capability depends on 'access structure'—specifically, hybrid architectures combining compressive state channels with scalable verbatim-index channels.

agentic-systems

The Steering Budget: Examples beat Knobs

Generative AI models are traditionally steered using 'knobs' like prompts, but they hit a hard ceiling. A new study reveals that providing concrete examples instead of adjusting prompts unlocks the model's full 'steering budget,' offering far greater reach and expressiveness.

agentic-systems

HG-RAG: Hierarchy-Guided Retrieval-Augmented Generation for Structured Knowledge Graphs

HG-RAG (Hierarchy-Guided Retrieval-Augmented Generation) is a novel framework designed to overcome the limitations of traditional flat RAG systems by traversing hierarchical knowledge graphs, significantly improving multi-hop reasoning and reducing hallucinations in LLMs.

agentic-systems

Enhancing Small Language Models Reasoning through Knowledge Graph Grounding

Researchers have proposed a neuro-symbolic framework that enhances the reasoning capabilities of Small Language Models (SLMs) like Gemma 3 and Llama 3.2 using Knowledge Graphs, achieving up to a 2x performance boost.

agentic-systems

Orchestrating Power Grid Studies with Multi-Agent AI and MCP Servers

This paper explores how Agentic AI and Model Context Protocol (MCP) can support power-grid studies by integrating LLMs with numerical simulation tools under human supervision.

agentic-systems

Human AI Construction of Bayesian Networks for Operational Decision Support -- A Virtual Survey Approach

Researchers propose a new methodology using Large Language Models and AI agents to construct Bayesian Belief Networks, bridging the gap between expert opinion and data-driven learning. The framework is demonstrated through a healthcare decision-making model, revealing key insights into customer behavior.