CANAO: A Cloud-Aware Native Agentic AI Framework for Adaptive Task Orchestration in Cloud-Native Environments

Agentic AI has emerged as a promising paradigm for autonomous reasoning and execution in complex AI-driven applications; however, its effective deployment in cloud-native environments remains challenging due to the lack of unified platform architectures that jointly support task decomposition, multi-agent collaboration, and adaptive cloud resource orchestration. In practical scenarios such as automated data analytics, AI DevOps, and MLOps pipelines, Agentic AI systems must operate over dynamic containerized infrastructures where resource availability, execution cost, and failure conditions continuously change. Existing approaches typically decouple agent-level decision making from cloud-native scheduling, resulting in limited scalability and poor robustness. To address these limitations, this paper proposes CANAO, a Cloud-Aware Native Agentic AI framework for adaptive task orchestration in cloud-native environments. CANAO models complex AI workloads as dynamically reconfigurable task dependency graphs and enables coordinated collaboration among Planner, Executor, and Critic agents. By incorporating real-time cloud resource awareness into the agent orchestration loop, CANAO supports adaptive scheduling, partial task re-planning, and self-healing execution on Kubernetes-based platforms. A prototype system is implemented using cloud-native technologies and evaluated on representative automated data analysis and AI DevOps workflows. Experimental results show that CANAO significantly outperforms baseline orchestration methods under dynamic cloud conditions. Compared with static DAG-based scheduling, CANAO reduces end-to-end task execution time by approximately 34.3% and cloud resource cost by nearly 30%, while lowering the task failure rate by over 34%. These improvements demonstrate the effectiveness of cloud-aware agent collaboration and adaptive task orchestration in large-scale cloud-native AI workflows.