The significance of Kiso becomes clearer when viewed against the broader history of scientific workflow systems and research infrastructure. For more than two decades, workflow management systems have served as the connective tissue of computational science, allowing researchers to express complex analysis pipelines as directed graphs of tasks while delegating the mechanics of scheduling, data movement, and failure recovery to specialized software. What has changed in recent years is the character of the underlying infrastructure. Experiments no longer run on a single cluster or a single cloud account; they increasingly span instruments, edge devices, institutional clusters, national testbeds, and commercial cloud providers, each with its own authentication mechanisms, resource models, and failure modes. This heterogeneity is precisely the condition under which manual configuration breaks down, and it is the condition that Kiso was designed to address by treating provisioning, configuration, and execution as declarative, repeatable operations rather than artisanal ones.

The reproducibility argument deserves particular attention. In computational science, reproducibility is often discussed in terms of software versions, container images, and data provenance, but the environment in which an experiment runs—the topology of machines, the network paths between them, the placement of services—can be just as consequential. Two researchers running the same workflow code on differently configured infrastructure may observe different performance characteristics, different failure patterns, and even different scientific outcomes. By capturing infrastructure provisioning and experiment configuration in declarative specifications, Kiso extends the reproducibility envelope downward through the software stack, so that the entire experimental context, from bare metal allocation to workflow execution, can be described, shared, and re-instantiated. This aligns with a growing movement in the computational sciences to treat experimental infrastructure as a first-class artifact of research, subject to the same standards of documentation and version control as analysis code.

The framework’s reliance on EnOSlib reflects a pragmatic design philosophy. Rather than reimplementing low-level interactions with each testbed, Kiso builds on a library that already abstracts over multiple experimental platforms, allowing it to target Chameleon’s cloud resources, edge devices, and other backends through a common interface. This layering has practical consequences for researchers: a change of testbed does not necessarily require a rewrite of the experiment description, and new platforms can be incorporated as the underlying library expands its coverage. It also insulates the framework from the churn inherent in research infrastructure, where hardware is refreshed, services are migrated, and interfaces evolve over timescales much shorter than the typical research project.

The choice of HTCondor as the coordination substrate for Pegasus workflows is similarly consequential. Traditional HPC schedulers assume a shared filesystem and relatively homogeneous, tightly coupled resources—assumptions that fail on the edge-to-cloud continuum, where edge nodes may be intermittently connected, bandwidth-limited, and geographically dispersed. HTCondor’s architecture, developed over decades of distributed high-throughput computing research, was designed for exactly this kind of loosely coupled federation: workers can join and leave, network partitions can be tolerated, and the matchmaking process can place jobs on whatever resources are available without requiring a global filesystem. For edge-to-cloud experiments, this means that a sensor node in the field and a virtual machine in a cloud data center can participate in the same workflow execution without artificial synchronization barriers, and that transient outages at the edge do not necessarily abort the entire experiment.

The extension toward agentic experiments represents a natural but nontrivial evolution. Multi-agent systems, particularly those built on large language models and other reasoning components, impose demands that differ from traditional data-intensive workflows. Agents may need to communicate in patterns that are not known at planning time, adapt their behavior based on intermediate results, and negotiate access to shared resources in real time. The framework’s declarative provisioning and extensible task abstractions provide a substrate for these patterns: agents can be deployed as containerized services on provisioned resources, communication channels can be established across sites during configuration, and monitoring and output collection can capture the traces of agent interaction that researchers need for analysis. The COLMENA work cited in the article illustrates this direction, demonstrating how agent-based applications can be deployed and studied on distributed infrastructure using the same foundations that support conventional workflows.

This convergence of workflow management and agent-based computing has implications for complex systems science more broadly. Agent-based modeling has long been a staple of complexity research, but such models have typically been simulated on single machines or clusters under controlled conditions. The emerging generation of agentic frameworks—in which autonomous agents powered by machine learning models plan, negotiate, and adapt—raises the possibility of studying self-organizing behavior in real cyber-physical systems rather than purely in simulation. Such studies require exactly the capabilities Kiso provides: consistent resource provisioning across heterogeneous sites, reliable cross-site communication, systematic collection of results and telemetry, and the ability to repeat an experimental configuration under controlled variation. In this sense, the framework can be read as infrastructure for a new class of complex systems experiments, one in which the adaptive behavior of interest is embodied in deployed software agents rather than abstract simulation entities.

The edge-to-cloud continuum itself is worth examining more closely. Edge devices—IoT sensors, embedded computers, vehicles, environmental monitoring stations—generate data at the point of observation, and processing that data locally can preserve privacy, reduce bandwidth demands, and enable low-latency responses. Yet edge devices are constrained in compute, memory, and power, so computationally intensive analysis must be offloaded to cloud resources. The scientific questions that arise from this architecture—how to schedule tasks across the continuum, how to tolerate intermittent connectivity, how to balance latency against throughput—are precisely the questions that require realistic testbed experimentation rather than pure simulation. Simulation remains valuable for exploring parameter spaces cheaply, but it inevitably abstracts away the network dynamics, hardware variability, and environmental noise that dominate real deployments. Testbed-based experiments complement simulation by exposing solutions to these realities, and frameworks like Kiso lower the barrier to conducting such experiments systematically.

The human dimension of this problem should not be underestimated. Research groups are typically composed of domain scientists—biologists, geoscientists, ecologists—whose expertise lies in their science rather than in systems administration. When an experiment requires provisioning virtual machines, configuring network routes, installing software stacks, and debugging connectivity across sites, the systems burden can consume weeks of effort and introduce silent errors that compromise results. Historically, this burden has been absorbed either by dedicated research software engineers, who are a scarce resource, or by domain scientists improvising solutions of variable quality. By automating provisioning and configuration, Kiso redistributes this burden: the expertise needed to set up a distributed experiment is encoded once in the framework and its declarative specifications, then reused by every researcher who adopts it. This democratization of distributed experimentation may prove as important as any technical capability, since it determines who can participate in edge-to-cloud research at all.

The modular architecture of the framework also positions it well for future evolution. Because infrastructure provisioning, deployment, experiment execution, and monitoring are separated into distinct layers, each can improve independently. New testbeds can be added as provisioning backends; new workflow systems can be supported alongside Pegasus; new agent frameworks can be integrated as task types; new monitoring approaches can be adopted as instrumentation needs grow. This modularity reflects lessons learned from earlier generations of scientific software, where monolithic designs often struggled to keep pace with changing infrastructure landscapes. It also means that Kiso can serve as a stable foundation while the agentic AI ecosystem, which is evolving extremely rapidly, continues to shift beneath it.

Looking forward, several research directions follow naturally from this work. One is the systematic study of fault tolerance and adaptation in edge-to-cloud workflows, where the framework’s ability to provision controlled topologies makes it possible to inject failures and observe system responses under repeatable conditions. Another is the empirical investigation of multi-agent coordination at scale, where questions about communication overhead, emergent behavior, and resource contention can be addressed with real deployments rather than idealized models. A third is the integration of measurement and telemetry into the experimental loop, so that the monitoring capabilities built into the framework become not just a convenience but a scientific instrument in their own right. In each of these areas, the combination of declarative provisioning, containerized services, and workflow-level orchestration offers a methodological template: experiments as reproducible, versioned, and shareable artifacts spanning the full edge-to-cloud continuum.

Ultimately, the contribution of Kiso lies less in any single technical mechanism than in the unification it achieves. Provisioning, configuration, execution, and monitoring have each been addressed by existing tools, but the gap between them is where much of the friction of distributed experimentation has lived. By closing that gap within a single coherent framework, and by extending it toward the emerging demands of agentic computing, the work offers the complex systems community a foundation on which the next generation of adaptive, self-organizing, and geographically distributed experiments can be built—and, crucially, repeated.

Subject of Research: …

Article Title: Kiso: a foundation for complex, agentic, and reproducible experiments

Article References: Mayani, R., Vahi, K., Rynge, M., Thareja, K., Casas-Moreno, X., Jin, H., Mandal, A., Lordan, F., Raghavan, K., Badia, R. M., & Deelman, E. (2026). Kiso: a foundation for complex, agentic, and reproducible experiments. Frontiers in Complex Systems, 4, Article 1800335. https://doi.org/10.3389/fcpxs.2026.1800335

Image Credits: AI Generated

DOI: 10.3389/fcpxs.2026.1800335

Keywords: Kiso, edge-to-cloud computing, scientific workflows, reproducibility, multi-agent systems, HTCondor, Pegasus WMS, FABRIC testbed, Chameleon Cloud, COLMENA, distributed computing, cyber-physical systems