Multi-Agent Human-AI Systems with Low-Code Platforms Enabling Adaptive Web Services and Real-Time Anomaly Remediation in Distributed Architectures

Modern distributed web architectures face escalating challenges from unpredictable anomalies and dynamic user demands, where traditional monitoring and remediation fall short in delivering real-time resilience. This paper presents a novel multi-agent human-AI framework integrated with low-code platforms that autonomously adapts web services while executing proactive anomaly remediation across hybrid edge-cloud environments. Specialized AI agents handle detection, root-cause analysis, and recovery orchestration, augmented by human oversight through visual workflow builders that accelerate deployment from design to production. The system leverages neuro-symbolic reasoning for explainable personalization, generative models for dynamic content, and zero-trust protocols for secure agent coordination. Extensive evaluations on Kubernetes-based simulations with Chaos Mesh workloads demonstrate 45% reduction in mean time to recovery (MTTR), 30% latency improvements under peak loads, and 99.8% uptime outperforming baselines like Prometheus Alertmanager and Istio service mesh by wide margins. This work advances autonomous DevOps by democratizing AI-driven engineering, enabling non-experts to orchestrate resilient distributed systems with minimal coding overhead. Our contributions position low-code multi-agent systems as foundational for Industry 5.0 web services, with pathways toward quantum-safe extensions and federated deployments.