REVIEW 5 cited by
Toward Edge General Intelligence with Agentic AI and Agentification: Concepts, Technologies, and Future Directions
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Toward Edge General Intelligence with Agentic AI and Agentification: Concepts, Technologies, and Future Directions
read the original abstract
The rapid expansion of sixth-generation (6G) wireless networks and the Internet of Things (IoT) has catalyzed the evolution from centralized cloud intelligence towards decentralized edge general intelligence. However, traditional edge intelligence methods, characterized by static models and limited cognitive autonomy, fail to address the dynamic, heterogeneous, and resource-constrained scenarios inherent to emerging edge networks. Agentic artificial intelligence (Agentic AI) emerges as a transformative solution, enabling edge systems to autonomously perceive multimodal environments, reason contextually, and adapt proactively through continuous perception-reasoning-action loops. In this context, the agentification of edge intelligence serves as a key paradigm shift, where distributed entities evolve into autonomous agents capable of collaboration and continual adaptation. This paper presents a comprehensive survey dedicated to Agentic AI and agentification frameworks tailored explicitly for edge general intelligence. First, we systematically introduce foundational concepts and clarify distinctions from traditional edge intelligence paradigms. Second, we analyze important enabling technologies, including compact model compression, energy-aware computing strategies, robust connectivity frameworks, and advanced knowledge representation and reasoning mechanisms. Third, we provide representative case studies demonstrating Agentic AI's capabilities in low-altitude economy networks, intent-driven networking, vehicular networks, and human-centric service provisioning, supported by numerical evaluations. Furthermore, we identify current research challenges, review emerging open-source platforms, and highlight promising future research directions to guide robust, scalable, and trustworthy Agentic AI deployments for next-generation edge environments.
Forward citations
Cited by 5 Pith papers
-
Agentic AI-Enhanced Semantic Communications: Foundations, Architecture, and Applications
A survey-plus-case-study proposing a three-layer agentic-AI architecture for semantic communications, with an LLM/LVM-built source knowledge base and RL-built channel knowledge base reporting a 9% PSNR gain over NTSCC.
-
Wireless Copilot: An AI-Powered Partner for Navigating Next-Generation Wireless Complexity
Introduces a human-in-the-loop 'Wireless Copilot' framework for 6G network management, with a LAWNets simulation showing higher intent satisfaction than LLM/RL baselines.
-
Agentic AI for ISAC: Analysis, Framework, and Case Study
An agentic ISAC framework using a transformer-based MoE policy and an LLM-designed reward reports 131% higher communication rate and 5.4% lower CRB than a SAC baseline in a small beamforming case study.
-
Reflection-Driven Self-Optimization 6G Agentic AI RAN via Simulation-in-the-Loop Workflows
A reflection-driven framework with scenario, solver, simulation, and reflector agents uses simulation-in-the-loop to create self-correcting agentic AI for 6G RAN, reporting 17.1% throughput gains and other improvements.
-
Clustered Edge Intelligence: Beyond Just Convergence of Edge Computing and AI
Clustered Edge Intelligence reframes edge AI as managing and clustering derived intelligence as independent entities rather than clustering the devices that produce it.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.