Distributed Agentic AI Framework for Autonomous Edge-Cloud Service Orchestration

Orchestrating services across heterogeneous 6G edge-cloud infrastructures requires autonomous coordination systems managing distributed computational resources while satisfying Quality-of-Service (QoS) requirements. Recent advances in Large Language Models (LLMs) enable development of autonomous agents capable of complex reasoning and decision-making for such orchestration tasks. However, applying generic agentic AI frameworks from the machine learning literature to orchestration domains introduces reliability limitations, as trial-and-error decision patterns are unsuitable for environments where errors disrupt services. This work presents AgentEdge, a novel distributed intelligence framework that implements specialized autonomous agents in four orchestration roles: intent processing, infrastructure monitoring, strategic planning, and action execution. AgentEdge introduces the PARES (Perceive, Act, Reason, Evaluate, Sustain) framework establishing minimum capabilities required for autonomous agent qualification. Central to AgentEdge is the ActSimCrit (Action-Simulation-Critic) planning methodology, which validates orchestration plans through digital twin simulation before execution, eliminating direct infrastructure experimentation risks. Agents coordinate multi-step operations and adapt strategies based on constraint feedback. Structured outputs constrain agent decision spaces to feasible orchestration actions while preserving optimization flexibility. Experimental evaluation in six orchestration scenarios validates AgentEdge through comparison with baseline agentic frameworks and ablation studies. AgentEdge achieves $2.76\times $ higher success rate compared to generic agentic frameworks (ReAct, LATS) and $10\times $ reduction in API call variability. The core ActSimCrit digital twin component alone contributes $1.47\times $ success improvement when compared to direct planning without simulation. AgentEdge achieves significant power savings across infrastructure scales from 8 to 35 nodes.