arXiv:2602.24115v2 Announce Type: replace Abstract: Open RAN (O-RAN) exposes rich control and telemetry interfaces across the Non-RT RIC, Near-RT RIC, and distributed units, but also makes it harder to operate multi-tenant, multi-objective RANs in a safe and auditable manner. In parallel, agentic AI systems with explicit planning, tool use, memory, and self-management offer a natural way to structure long-lived control loops. This article surveys how such agentic controllers can be brought into O-RAN: we review the O-RAN architecture, contrast agentic controllers with conventional ML/RL xApps,
The increasing complexity of Open RAN architectures and the rapid development of agentic AI systems are converging, making sophisticated autonomous control a timely and necessary solution.
This development indicates a significant push towards more autonomous and intelligent network management, which will reshape telecommunications infrastructure and its operational models.
Network operations will move from manual or rule-based systems to self-evolving AI agents, potentially leading to more efficient, adaptable, but also more opaque and complex RANs.
- · Telecom operators adopting agentic AI
- · AI software providers specializing in autonomous agents
- · Open RAN vendors that integrate agentic capabilities
- · Traditional network management software providers
- · Organizations slow to adopt AI-driven operations
- · Operators with rigid, non-open RAN infrastructures
Open RANs become more efficient and capable of handling multi-tenant, multi-objective demands autonomously.
The demand for skilled AI engineers in telecom increases, while the need for traditional network operations staff shifts towards AI supervision and auditing.
The development of truly 'self-healing' and 'self-optimizing' global communication networks becomes a plausible reality, creating new dependencies and cybersecurity challenges.
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