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

Original: arXiv:2605.27575v2 Announce Type: replace 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.

Rewrite: arXiv:2605.27575v2 Announce Type: replace Abstract: With enterprises transitioning AI agents into production environments, the focus of engineering efforts is evolving. Because these agents handle non-deterministic tasks, retain stateful sessions, and frequently require privileged access to internal systems, the primary challenge is no longer just creating individual agents but managing them at scale with robust isolation, governance, and security. This paper introduces Agyn, an open-source platform built on three core pillars optimized for agent workloads: a signal-driven, stateful serverless runtime operating on Kubernetes; a Terraform provider that enables agent and harness definition as code; and a security framework based on zero-trust and least-privilege tenets. Agyn remains agnostic to agents, models, and cloud providers.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC