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Abstract

Large language models are increasingly being used to support network operations (NetOps) and artificial intelligence for IT operations (AIOps), including incident investigation, root-cause analysis, configuration synthesis, and limited self-healing. In both NetOps and AIOps, this shift is changing how tasks are managed. Agent-based operations work as workflows, from gathering evidence to taking action, following permissions, policies, and checks, and providing rollback options when necessary. This is crucial because operational decisions can have immediate effects on live systems. It depends on mediated evidence, controlled change, and accountable recovery. To make the argument concrete, we organise the relevant literature around the hierarchy of autonomy, tool scope, evidence traces, and assurance contracts. These contracts define what an agent may observe, propose, and execute. They also define the checks that must pass before any action is allowed. A consistent pattern appears across work on telemetry query recommendation, diagnosis, root-cause analysis, configuration synthesis, change planning, and limited self-healing. Operational reliability does not come chiefly from the model itself. Safe operation depends on the machinery around the model: typed tool interfaces, provenance- and freshness-aware retrieval, explicit budgets and stopping rules, least- privilege access, and verification gates at the write boundary that the agent cannot bypass. We also argue that evaluation should go beyond static question answering. Agentic NetOps and AIOps systems require workflow- centred evaluation, including trace quality, bounded tool use, safe proposal generation, replay in sandboxed environments, and canary trials with rollback-aware scoring. Without these measures, a system may appear robust in demonstration yet remain too fragile when real infrastructure can be changed. Finally, we examine security, privacy, and governance risks that become acute when agents sit close to operational control surfaces. These include prompt injection through operational artefacts, retrieval poisoning, telemetry integrity attacks, excessive agency, and weak auditability. Taken together, the survey concludes that progress in intelligent NetOps and AIOps will depend on treating autonomy as a constrained operational control problem, whose outputs must be reliable, auditable, and securely deployable.