Milos Gravara

Milos Gravara, Juan Luis Herrera, Stefan Nastic

Compound AI is a distributed intelligence approach that represents a unified system orchestrating specialized AI/ML models with engineered software components into AI workflows. Compound AI production deployments must satisfy accuracy, latency, and cost objectives under varying loads. However, many deployments operate on fixed infrastructure where horizontal scaling is not viable. Existing approaches optimize solely for accuracy and do not consider changes in workload conditions. We observe that compound AI systems can switch between configurations to fit infrastructure capacity, trading accuracy for latency based on current load. This requires discovering multiple Pareto-optimal configurations from a combinatorial search space and determining when to switch between them at runtime. We present Compass, a novel framework that enables dynamic configuration switching through offline optimization and online adaptation. Compass consists of three components: COMPASS-V algorithm for configuration discovery, Planner for switching policy derivation, and Elastico Controller for runtime adaptation. COMPASS-V discovers accuracy-feasible configurations using finite-difference guided search and a combination of hill-climbing and lateral expansion. Planner profiles these configurations on target hardware and derives switching policies using a queuing theory based model. Elastico monitors queue depth and switches configurations based on derived thresholds. Across two compound AI workflows, COMPASS-V achieves 100% recall while reducing configuration evaluations by 57.5% on average compared to exhaustive search, with efficiency gains reaching 95.3% at tight accuracy thresholds. Runtime adaptation achieves 90-98% SLO compliance under dynamic load patterns, improving SLO compliance by 71.6% over static high-accuracy baselines, while simultaneously improving accuracy by 3-5% over static fast baselines.

Milos Gravara, Andrija Stanisic, Stefan Nastic

The 3D continuum presents a complex environment that spans the terrestrial, aerial and space domains, with 6Gnetworks serving as a key enabling technology. Current AI approaches for network management rely on monolithic models that fail to capture cross-domain interactions, lack adaptability,and demand prohibitive computational resources. This paper presents a formal model of Compound AI systems, introducing a novel tripartite framework that decomposes complex tasks into specialized, interoperable modules. The proposed modular architecture provides essential capabilities to address the unique challenges of 6G networks in the 3D continuum, where heterogeneous components require coordinated, yet distributed, intelligence. This approach introduces a fundamental trade-off between model and system performance, which must be carefully addressed. Furthermore, we identify key challenges faced by Compound AI systems within 6G networks operating in the 3D continuum, including cross-domain resource orchestration, adaptation to dynamic topologies, and the maintenance of consistent AI service quality across heterogeneous environments.