|---Module:text|Size:Small---| In the fast-changing telecommunications sector, AI is becoming a foundational element in driving transformation. Celfocus has collaborated with Mobile Europe and technology and business journalist Sue Tabbitt, to develop this exclusive market research, designed to provide clarity and actionable insights on where Autonomous Network Operations (ANOps) investment delivers the biggest impact, who’s making progress, and what are the next steps for operators aiming for true Autonomous Operations.
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How is agentic AI different from other kinds of AI and how can telcos prepare for it?
Gartner positions agentic AI at the leading edge of the AI evolution. Traditional (analytical) AI and LLM-based systems act when prompted by human inputs. They don’t initiate actions.
Agentic AI can receive and act on high-level goals, break them down into steps, select appropriate tools, act, and adapt based on outcomes. These systems can learn from their environment, make decisions and perform tasks independently.
Across all sectors, not just telecoms, Gartner predicts that:
- By 2028, 33% of enterprise software applications will include agentic AI and that at least 15% of daily work decisions will be made autonomously
- By 2029, 70% of enterprises will deploy agentic AI as part of IT infrastructure operations, up from less than 5% in 2025
- By 2035, at best agentic AI could drive about 30% of software revenue for enterprise applications, surpassing $450 billion.
Moving to scalable agentic autonomous networks
Telco networks are becoming smarter but also more complex to manage, with increasing skill gaps and slower fault resolution. Agentic AI closes the loop by linking high-level reasoning to real-time action, automating diagnosis, coordinating workflows, reducing mean-time-to-repair and human workload, and preserving institutional knowledge at scale.
Its effectiveness depends less on the AI models themselves and more on a strong data foundation. “We advocate for a layered approach, a substrate of structured world knowledge at the base, with intelligence (ML, LLMs, algorithms) and agency (agent frameworks) built on top of it — each layer doing what it does best.” Agents work in a loop: observe, decide, act, verify, deliver value. Every step in that loop needs a queryable, consistent, time-aware view of the network.
This is where the digital twin becomes essential. For this foundation to support agentic workloads, it should deliver four key capabilities:
- Structured topological retrieval
- Temporal depth
- Validation surface
- Multi-vendor abstraction
|---Module:text|Size:Small---| Ultimately, Agentic AI delivers real value when it is built on a solid foundation. The benefits are clear: incidents resolved before customers notice, changes delivered in hours instead of weeks, institutional knowledge that survives the next retirement wave, and engineers are freed from repetitive correlation tasks. However, this transformation doesn’t happen by accident nor through prompt engineering alone.
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Curious to know more? Access the full report here and explore the Celfocus feature on pages 19 to 27.
