Managing AI Agents Is the Core Skill in Autonomous Networks
Why It Matters
Because AI‑driven agents will become the core operational layer of future networks, operators that master agent governance and workforce transformation will secure cost efficiencies and market leadership.
Key Takeaways
- •Autonomous networks span planning, deployment, operation, and decommissioning.
- •AI agents now dominate radio optimization and fault remediation.
- •Operator trust drives shift from human‑in‑the‑loop to full autonomy.
- •Managing AI agents requires prompt engineering and clear governance policies.
- •Mid‑career staff adoption gap hampers enterprise‑wide AI transformation.
Summary
The Pulse interview with Marcus Nesper, Extreme Networks’ head of the CTO office, frames autonomous networks as an end‑to‑end lifecycle—from planning and onboarding through operation, optimization, and eventual decommissioning—driven by AI‑enabled agents.
Nesper notes that while full L4 autonomy remains limited, AI agents have already taken control of radio‑network optimization and accelerated fault detection and remediation. In many operators, AI functions as a co‑worker, sifting through complex telemetry to pinpoint root causes faster than manual processes.
He emphasizes that “trust is a moving target” and that agents must operate under clear governance, akin to managing a team. The shift requires engineers to master prompt engineering, set boundaries, and intervene only when agents request clarification, turning orchestration into agent‑management.
For telecom operators, the transition means re‑thinking workflows, upskilling staff, and establishing policy frameworks. Those that successfully integrate AI agents can achieve lower operational costs, higher network performance, and a competitive edge in the rapidly evolving 5G and beyond landscape.
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