The work, published in Frontiers in Complex Systems, extends a well-known distributed computing concept—consensus—into a new territory that the authors call the Compute Continuum. This continuum spans everything from tiny Internet of Things devices and edge servers to cloud data centers and high-performance computing clusters. As applications become hyper-distributed across these heterogeneous resources, the researchers argue, the traditional model of a centralized orchestrator that makes all scheduling and deployment decisions becomes a liability. Centralized control planes introduce single points of failure, struggle with volatile environments, and add communication overhead that undermines latency-sensitive applications. The alternative is decentralized coordination, where autonomous agents cooperate to achieve common goals and dynamically distribute workloads among themselves.

Consensus algorithms are the mathematical backbone of this vision. In distributed systems, consensus protocols allow multiple independent agents to reach agreement on a shared decision even when messages arrive out of order or some agents fail outright. Classical protocols such as Practical Byzantine Fault Tolerance, Raft, and Paxos have long underpinned leader election, fault tolerance, and replicated state management. The team built on a PBFT-inspired mechanism previously developed for decentralized job selection in workflow management systems, where agents collectively pick assignments by sharing cost matrices based on available resources such as CPU, RAM, and disk. But the researchers faced a fundamental complication: unlike jobs, which sit in a globally visible pool, the roles that agents assume in swarm environments are triggered locally, in response to real-time quality-of-service conditions. There is no global view of all required roles; consensus is reached only when a sufficient number of agents independently determine that a role is needed.

To support this model, the team leveraged COLMENA, a swarm-based programming environment originally developed at the Barcelona Supercomputing Center. COLMENA decomposes services into roles—executable, application-specific functionalities that solve particular tasks—which can be dynamically activated or terminated by agents themselves. Each agent runs a stack integrating context awareness, decision-making, and quality-of-service monitoring, using Prometheus for QoS evaluation, Zenoh for name-centric networking and discovery, and ZeroMQ for communication. Key performance indicators, formally specified in Prometheus Query Language, define thresholds that determine whether the desired service level is satisfied or violated. When a KPI breaks, agents run decision-making policies to start or stop roles, and in this work those policies are driven by consensus.

The consensus mechanism itself follows a three-phase protocol of propose, prepare, and commit. When a KPI violation suggests that a role should be executed, the initiating agent does not act unilaterally. Instead, it broadcasts its candidacy along with a computed cost value that quantifies its suitability for the role by comparing the role’s resource demands to its available capacities. During the prepare phase, agents evaluate incoming proposals, vote for the best candidate, and share their votes with peers. Once a quorum—typically a majority of participating agents—has voted for the same candidate, the protocol enters the commit phase, where agents confirm their final agreement. Only the elected leader, the agent with the lowest cost, proceeds to execute the role, while the others record the outcome and continue monitoring. The cost function penalizes agents that heavily stress a single resource, favoring those with more balanced availability, and can be extended with terms such as network proximity or trust metrics.

To demonstrate the approach, the researchers turned to modern power systems, which are increasingly decentralized due to the large-scale integration of renewable energy sources. Centralized power plants are giving way to thousands of distributed, intermittent resources that require rapid, localized responses without relying on centralized control. The team simulated the Northeastern Power Coordinating Council’s 140-bus benchmark grid—a widely used representation of the power grid of the northeastern United States and parts of Canada, with 140 buses, 48 generators, and 92 loads—using the open-source ANDES power system analysis tool. The network was partitioned into six distinct regions, each corresponding to a different Independent System Operator, and COLMENA agents were assigned to monitor frequency deviations within each area.

The corrective control algorithm is a Distributed Model Predictive Control formulation, in which each area solves a local optimization problem while coordinating with neighboring areas to ensure global consistency. The grid is modeled as a set of interconnected areas characterized by two state variables—system frequency and voltage angle—whose evolution follows a discretized version of the swing equation. Consistency across areas is enforced through coupling constraints on shared variables, resolved using the Alternating Direction Method of Multipliers, a distributed optimization technique in which agents iteratively exchange local estimates of shared variables and associated dual variables until convergence. In COLMENA, the MPC is not deployed as a continuously active controller; instead, it is encapsulated as a role that is activated only when a frequency-related KPI is violated, allowing it to coexist with other control strategies such as local proportional-integral controllers or grid-forming converter control, each operating on different time scales.

The experiments were deployed across six geographically distributed sites of the FABRIC testbed, a national-scale programmable network infrastructure funded by the U.S. National Science Foundation, stretching from Los Angeles and San Diego to Starlight in Chicago, Florida, Bristol in the United Kingdom, and Hawai’i. The team scaled the number of agents per area from three to ten, reaching up to 60 nodes in total, each with four CPU cores and 16 gigabytes of RAM. Two types of contingencies were tested: disconnecting ten loads from the grid, and disconnecting two of the highest-inertia generators. To ensure reproducibility, the entire experimental workflow was automated through Kiso, an open-source experiment management platform that expresses deployments as declarative YAML specifications, provisioning resources, configuring software, and triggering experiments as ordered sequences of labeled tasks.

The results are striking. Under contingency scenarios, agents self-organized, elected local leaders, and executed the optimization algorithms to stabilize grid frequency. Detection and organization times remained below 10 seconds across all experiments, even as the number of agents per area scaled from 3 to 10. Stability was restored within approximately 27 seconds for load disconnections and 40 seconds for generator disconnections—a dramatic improvement over conventional secondary frequency control schemes, which typically require up to 15 minutes for full deployment. In the generator case, the conventional response proved insufficient, leaving frequency below 0.998 of nominal, whereas the agent-coordinated response successfully restored it above 0.999. Resource overhead was minimal, with CPU usage remaining below 7.5 percent and memory usage below 2 percent. Consensus times increased only slightly as the swarm grew, since the protocol simply had to process more votes, while detection times rose more noticeably because metric aggregation was performed over all 60 agents.

The findings carry implications well beyond power grids. The authors argue that role-based programming models complement traditional workflow systems, enabling adaptive execution of hyper-distributed services, and that consensus can be treated as a reusable coordination primitive in swarm computing—a backbone on which more advanced decision-making techniques, from bio-inspired algorithms to multi-agent reinforcement learning and agentic AI based on large language models, can be layered. For extremely large-scale scenarios with hundreds of thousands of nodes, the message complexity of PBFT-based protocols could become a limitation, and choreography-based approaches where agents coordinate through emergent alignment may complement consensus where latency is critical. But when systems require agreement on specific role executions, strong consistency guarantees, or robustness against failures, consensus-driven coordination remains essential. As electrical grids continue their transformation toward distributed renewables, microgrids, and prosumers, the ability of autonomous agents to monitor, negotiate, and act in seconds—without a central authority—could prove a decisive step toward resilient, decentralized power systems.

Subject of Research: Decentralized coordination of autonomous agents in the Compute Continuum using consensus

Article Title: Decentralized coordination of autonomous agents in the Compute Continuum using consensus

Article References: Casas-Moreno, X., Thareja, K., de Juan Vela, P., Mayani, R., Fanals-i-Batllori, J., Mandal, A., Deelman, E., Badia, R. M., & Lordan, F. (2026). Decentralized coordination of autonomous agents in the Compute Continuum using consensus. Frontiers in Complex Systems, 4, Article 1800101. https://doi.org/10.3389/fcpxs.2026.1800101

Image Credits: AI Generated

DOI: 10.3389/fcpxs.2026.1800101

Keywords: consensus algorithms, autonomous agents, Compute Continuum, decentralized coordination, power grids, frequency control, COLMENA, multi-agent systems, Decentralized, coordination, autonomous, agents