💡 The paper introduces OpenRath, a programming model for multi-agent systems that addresses the issue of fragmented runtime state. In current agent systems, various aspects such as transcripts, tool effects, and memory events are recorded separately, making it difficult to inspect or reproduce the system's behavior. OpenRath solves this problem by introducing a central runtime abstraction called Session, which is a first-class value that can be passed between agents and workflows.

The Session abstraction is designed to be branchable, inspectable, replayable, backend-aware, and composable, allowing it to record various execution state information such as conversation chunks, sandbox placement, and tool evidence. This enables explicit fork, merge, and replay operations as runtime operations rather than reconstructing states from external traces.

OpenRath also defines other key concepts such as Sandbox, Tool, Agent, Memory, Workflow, and Selector, which work together to provide a comprehensive programming model for multi-agent systems. The Selector is particularly important as it turns control flow into runtime-routed decisions.

The paper presents the programming model, architecture, and evidence protocol of OpenRath, and claims that the Session abstraction provides agent systems with a first-class runtime value for auditable composition. The results of this work are limited to controlled runtime properties, and further evaluation is needed to compare the performance of OpenRath with other systems and to assess its availability and quality.

Overall, OpenRath contributes a novel programming model for multi-agent systems that provides a unified and explicit way to manage runtime state, making it easier to inspect, reproduce, and debug the behavior of these systems.

💡 The paper introduces AOHP, an open source operating system framework built on the Android Open Source Project, designed to support AI agents as first class entities. The motivation behind AOHP is to address the limitations of existing operating systems, which are application centric and do not provide native support for AI agents, resulting in execution overhead and safety risks. AOHP treats agents as first class OS actors, enabling adaptive user interfaces and agent friendly runtime environments. The framework introduces three agent oriented system mechanisms: personalized service composition, efficient agent interfaces, and secure information flow. The authors evaluated AOHP through preliminary experiments on challenging tasks and found that it shows significant advantages in task completion rate, execution cost, and security policy compliance. Specifically, AOHP achieved a 21.12 percent increase in task completion rate and a 51.55 percent reduction in token cost. The paper contributes to the research community by providing an open testbed to explore the architectural primitives desired for agent mediated interaction, and demonstrates the potential of AOHP to enhance the efficiency, security, and adoption of AI agents in operating systems.