HydraScale: Trust-Coupled Elastic Scaling for Federated CI/CD Pipelines Using Bounded Autonomous Agents

Continuous integration and delivery (CI/CD) pipelines increasingly execute across heterogeneous runner pools spanning standard, regulated, and isolated execution domains. Conventional autoscalers provision capacity using compute signals such as queue depth, CPU, or pending job count, but they ignore whether the target runner domain remains trusted. We define this mismatch as the scaling-security decoupling problem (SSDP): a pipeline controller may admit jobs or scale replicas into a domain whose runtime trust score has degraded below an acceptable threshold, causing unsafe admissions, compliance stalls, or provisioning deadlock. This paper presents HydraScale, a trust-coupled elastic scaling framework for federated CI/CD pipelines. HydraScale combines a four-component runtime trust vector with a three-tier bounded-autonomous control plane that couples queue, capacity, and trust signals before each scale-out, scale-in, or migration decision. We implement HydraScale as a service mesh of seven Python micro-services with Open Policy Agent enforcement, evaluate it against three baselines (static, queue-only predictive, reactive trust) across five scenarios, four scaling systems, and ten random seeds (200 paired runs), and report unsafe admissions, queuing stability, decision latency, and audit completeness. HydraScale admits no unsafe jobs across the five evaluated scenarios versus baseline means of 4.04 (reactive trust) to 204.56 (trust-blind), with Mann-Whitney U p ≤ 2.2 × 10−4 across the test family, p99 decision latency of 17 µs, and complete audit traces in all measured decisions.

Eliminates unsafe job admissions across all evaluated scenarios by coupling trust signals with elastic scaling decisions.

zero-trustCI/CDautoscalingKubernetesSPIFFEOPAagentic AIbounded autonomy