Deutsche Telekom Extends Agentic AI Beyond RAN

Deutsche Telekom is extending agentic AI from the radio network to network-wide operations, according to RCR Wireless on 21 October, reporting on DT's AI Investor Day. RAN Guardian, live in Germany since November 2025, uses 3 agents: one scans public and social media and other sources for events, one categorizes events and assesses their network impact, and one remediates ahead of the event through carrier aggregation, uplink adjustments and antenna tilt changes. A cross-domain platform called MINDR is in development, with an initial MVP in Germany by the end of 2026.

Capacity against realised results

Chief network officer Armin Sumesgutner said the agentic approach can analyze and remediate 100,000 events, against about 1,000 handled manually before, with a goal of avoiding 24 million minutes of poor customer experience. RAN Guardian triggered more than 100 remediation actions autonomously in its first month. That is a long way from the 100,000 events of capacity, and RCR does not report actions since. MINDR targets more than 90% automated ticket handling, issue qualification in under 15 minutes, incident resolution in under 1 hour and a 30% productivity improvement.

Level 4 with a human approver

DT states the goal as Level 4 autonomous networks. Group technology officer Arash Ashouriha said that for every critical change there will always be a human in the loop. In the TM Forum framework, Level 3 is conditional autonomy with human oversight, and Level 4 is intent-driven, closed-loop operation with minimal human intervention for routine tasks. Autonomous fixing of low-impact issues with human approval of critical ones fits that wording, but it means the level describes the routine tail and not the changes that carry risk. The approval boundary is therefore the design object: which changes count as critical, who holds the authority to make them, and what record is kept. That is the authority-boundary and audit-trail question in Agentic AI: Autonomous Agents Governance and Enforcement, and the reason I argued in DTW Ignite 2026: The API Is Not Enough that the API layer alone does not supply it.

Ericsson's Mats Karlsson told Fierce Network at the OSS/BSS Summit in Zurich on 20 October that AI automation is not network autonomy and that autonomy levels should not be treated as a numbers game. His example was that a Level 4 investment for a task performed twice a year may never pay back, while automating a task run 1,000 times a day changes the equation. DT's numbers fit that frame, since RAN Guardian earns on event volume. The figures still to be published are the actions executed to date, the share needing human involvement, and the minutes avoided measured against the 24 million goal.

Ericsson and Nokia at odds on AI-RAN silicon

Ericsson CEO Per Narvinger used the Q3 2026 earnings call on 15 October to defend custom ASICs for AI-RAN, according to RCR Wireless on 16 October. Asked by Deutsche Bank's Robert Sanders whether GPUs in the baseband had been ruled out, Narvinger said Ericsson is not philosophically against anything and will pursue what makes business sense. Its software and hardware are disaggregated, so GPUs remain an option, although they would require new hardware at the site. The function Ericsson is selling today, AI-native link adaptation, runs on basebands already deployed. Ericsson reports a 10% spectral efficiency gain and says the function runs on more than 60,000 base stations at 3 operators, including Optus, with AT&T, Bell Canada and T-Mobile reportedly testing it. All figures here come from RCR's account of the call. 

Narvinger put a 10% spectrum efficiency gain at about $800 million, against the $8 billion SpaceX was reported to have paid for spectrum the week before. RCR does not say what base the $800 million is calculated on, so I treat it as an illustration, not a measurement. The comparison does clarify the nature of the benefit. Spectral efficiency is capacity gained on spectrum already licensed, which is cost avoidance, the category I separated from new revenue in AI monetization for operators: separating revenue from cost avoidance. On a single cell the value arrives when a capacity upgrade is deferred, and Vish Nandlall's RCR Analyst Angle of 13 October put that at about $18,000 in present value for a cell at 91% load with a $200,000 upgrade deferred by about 1 year. An average gain across 60,000 sites says less than how many of those sites sit near an upgrade trigger.

Light Reading reported on 12 October that Nokia is building its AI-RAN on Nvidia GPUs and has stopped new custom silicon, with operators testing this quarter and commercial availability in early 2027. Ericsson's answer is that value can be delivered now on silicon operators already own. Both positions have an economic logic, and neither has been tested against the other in a published cost comparison. This is the gap between field-validated and commercially validated that I described in Is AI-RAN the future of telco?. A footprint of 60,000 sites is a field result for a software function on existing ASICs. It does not show that a GPU baseband earns its premium. Where accelerators are needed, the position in Non-RT RIC, AI-RAN, and the AI Grid applies: deployment starts where power, cooling and fibre are already in place, which points to the central office before the cell site.

Component costs add to the comparison. RCR reports that Ericsson beat Q3 forecasts on 7% organic sales growth, helped by customers buying ahead of expected AI-related price rises, and warned that AI-driven component costs will pressure margins through 2027. 

As you know if you follow this blog, I am skeptical of the cost-benefit of putting GPUs in a base station. The costs are high and growing, both form a capex and an energy perspective, while the benefits are entirely speculative. Would Nokia have taken the same architectural decision without NVIDIA's 1B$ investment? Unlikely. Between data centers, AI-RAN, sovereign AI, and AI factories, NVIDIA plays on a lot of different fields in telecom. They can afford for some of these explorations to backfire. Can Nokia afford to fail the next generation of radio architecture by betting it all on GPU?