Building on the foundations of conventional and generative artificial intelligence (GenAI), agentic artificial intelligence (Agentic AI) is fast emerging as the next big leap in the evolution of artificial intelligence (AI), entering an unexplored area with its capacity for autonomous, multi-step reasoning. Agentic AI goes beyond prediction models that propose results or react depending on learnt patterns; it acts on its own initiative.
Often, without continuous human oversight, these artificial intelligence bots may spot issues, create and weigh several answers, decide what to do, and carry out judgments. Particularly for its ability to transform how operators maximise infrastructure, solve network anomalies, lower operational loads, and improve customer experience with intelligent self-service technologies, this paradigm shift is generating great interest in the telecom industry.
Notwithstanding the futuristic promise, a fundamental concern remains: Is the telecom sector, which has always been hesitant to absorb unproven innovation and highly wary about losing control, ready to embrace a kind of artificial intelligence that questions its conservative DNA?
A synopsis of history
Plotting the history of artificial intelligence in telecoms helps one to see the relevance of agentic artificial intelligence. Early in the 2000s, phone companies started using rule-based systems for fraud detection and automated call routing.
Predictive analytics and machine learning first began to be applied to project network congestion, client turnover, and service demand by the 2010s. More recently, GenAI solutions have helped with content generation and natural language processing for consumer-facing applications.
However, their lack of contextual awareness and real-time execution capability hampered these instruments. Agentic artificial intelligence marks a new horizon whereby autonomous systems may not only evaluate but also act with agency, judgment, and adaptation.
Juniper Research’s observations suggest that agentic artificial intelligence has great potential in telecoms since it may assume challenging roles with less control. Network optimisation is one of the most important uses because agentic systems surpass mere problem prediction.
Through constant learning from patterns and adaptation, these agents may proactively fix network imbalances and preserve optimal performance conditions. Both security and fraud detection benefit equally; rather than depending on fixed rules, these artificial intelligence agents dynamically change security protocols to counter changing cyber threats, thus enhancing resilience in an era of ever-advanced attacks.
Virtual assistants driven by restricted GenAI or chatbot logic usually cannot independently address problems in customer service. Agentic AI, on the other hand, is poised to transform this field by allowing bots to not only comprehend but also act on sophisticated consumer needs, including account settings, refund requests, or technical problem solutions without agent escalation. Long praised but rarely smooth, personalisation becomes more flexible and responsive as AI systems continuously learn and customise services to real-time user behaviour.
Agentic artificial intelligence has a transforming edge in its change from static automation to autonomous orchestration. This new degree of capability brings contextual awareness and decision agency, therefore transcending artificial intelligence’s usual job as supporting analysis in the field of digital operations management. One European CTO said at MWC 2025, “Agentic AI is the brain we always wanted for our networks, but we’re still not sure if we trust it with the steering wheel.”
Existing telecom AI vs agentic AI
Although the phrase “agentic AI” sounds like a futuristic abstraction, its basic ideas are not wholly novel. For years, telcos have been testing network automation, particularly via Self-Organising Networks (SON) in the Radio Access Network (RAN), where artificial intelligence controls network tuning and resource allocation. Still, SON systems are essentially rule-based and reactive. Rather than making decisions on their own, they respond to premeditated stimuli.
Agentic artificial intelligence marks a further development. Targeting predictive correction and autonomous learning, these agents are not only reacting to events but also constantly scanning, hypothesising, and testing network configurations in the background.
This can cover application performance management, real-time orchestration across RAN and core networks, and anomaly prevention before user impact. The intricacy and seeming risk involved in handing important operational choices to nonhuman systems make the telecom sector understandably dubious, even with great theoretical appeal.
Industries like banking have been more aggressive with artificial intelligence autonomy by comparison. Though dangers vary, algorithmic trading in finance or smart manufacturing with dynamic supply chain changes shows that agentic systems are possible. The difference emphasises how adoption curves are shaped by sector-specific pressures and legal obligations.
ROI expectations and economic impact
Adopting agentic artificial intelligence has significant financial consequences, rather than only technical ones. Over the next ten years, industry observers estimate that intelligent automation in telecoms might release up to $200 billion in value. Improved client retention, better network use, and operational cost savings could account for a good portion of this value.
While tailored customer journeys driven by AI may boost ARPU (average revenue per user), AI-based predictive maintenance could help lower downtime and related service fines. Reducing the requirement for customer service escalation or manual network troubleshooting could also free up important human resources for more high-value projects. These efficiency improvements could be very important for long-term viability for carriers running slim margins.
Transparency in making ethical decisions
Ethical issues will surely become more pressing as agentic artificial intelligence finds an increasing presence in telecom operations. Should AI agents be allowed to provide traffic top priority during network congestion, and if so, under what standards? Might personalising algorithms that favour high-revenue consumers disadvantage some user groups? When AI decides on choices with social or political ramifications, including the undervaluation of connectivity in underdeveloped areas, what follows?
To meet these issues, openness and explainability will be crucial. Telcos will have to create systems that clearly justify their autonomous judgments as well as make them. The system design philosophy has to change from blackbox models toward interpretable and auditable AI systems. As artificial intelligence’s influence in telecom services grows, regulatory authorities will probably seek more responsibility.
Learnings from other domains
Telecoms have been quite cautious when implementing autonomous artificial intelligence systems compared to other industries. Algorithmic trading systems now make choices in milliseconds and run with almost no human involvement in finance. Smart manufacturers utilise artificial intelligence to dynamically change supply chains and maximise manufacturing processes depending on demand and logistics.
Often in high-stakes settings, even healthcare is starting to adopt AI-powered diagnostics and patient monitoring.
These industries present both successful blueprints and cautionary stories. Successful adoption of artificial intelligence depends on progressive implementation, well-defined return on investment, and a significant emphasis on ethical monitoring, among other common denominators.
Telecoms have to absorb these lessons by building trust through openness, implementing human-in-the-loop protections, and matching technology change with cultural and legal realities if they are to properly leverage agentic artificial intelligence.
Future applications beyond the standard
Although present agentic artificial intelligence applications in telecoms mostly address RAN optimisation, network performance, and customer service, their possibilities go much further. Agentic artificial intelligence could spread into more significant corporate roles in the next few years.
Dynamic pricing engines, for example, might independently change service plan rates using real-time network statistics and market trends. AI agents may also assist operators in managing spectrum allocation depending on changing traffic and geopolitical conditions or in reacting to the marketing of competitors.
Agentic artificial intelligence might also be involved in marketing, doing A/B tests on offers, and independently scaling the most successful campaigns or changing messaging depending on live sentiment analysis.
Autonomous systems could find abnormalities in bills and send proactive communications or corrections to consumers without human interaction. The potential of AI models to impact high-level business results will likely increase in parallel as they develop and train on multimodal data (from speech, usage behaviour, location, and CRM inputs).
Industry viewpoints are still split, but they are also growing interesting. Some CEOs on the ground at events like MWC 2025 predict agentic AI as unavoidable. Others exercise caution, however.
A Chief Technology Officer from a Scandinavian operator remarked, “We’re not looking to replace our engineers, but why not let an artificial intelligence spot and fix abnormalities faster than a NOC team?”
Analysts also agree: telcos must lean in rather than jump. Emphasising “measured experimentation” as a strategic approach to agentic AI, Gartner’s 2025 telecom tech view exhorts operators to “fail fast in contained zones” to build trust without losing stability.
Right now, the largest divide resides not in technology but in trust. Cultural changes and organisational buy-in will be essential to scale the promise of artificial intelligence from hype to daily use as agentic systems gradually establish themselves in constrained roles.
Execution is elusive
Although concrete, large-scale projects are still a year or two off, with 2026 expected as a likely turnaround point, some telcos have already dipped their fingers in the water. For example, recently, Google Cloud and Deutsche Telekom revealed a cooperation to test agentic artificial intelligence in a RAN context.
Without waiting for human input, this artificial intelligence agent is designed to spot performance problems in real-time and act immediately for correction. Enhanced network uptime, lower costs, more bandwidth stability, and better client experiences are among the expected results.
In the same vein, Telenor and Ericsson co-developed a proof-of-concept aimed at maximising RAN capacity and energy economy. Particularly in changing demand environments, a rising issue in 5G and soon-to-be 6G infrastructures, their usage of agentic AI promises to balance traffic loads more efficiently.
Industry insiders warn that these initiatives are still in controlled conditions and distant from being implemented over commercial networks, despite the hoopla around events like MWC 2025.
The unifying thread running throughout these early-stage initiatives is that agentic artificial intelligence is intriguing but still immature. Important unresolved issues still include performance under erratic pressure, interface with legacy systems, and openness of autonomous judgments. AI without explainability in decision paths could cause compliance problems.
Operators still demand authority
According to a recent Telecoms.com survey on 5G AI adoption, reliability, responsibility, and service assurance still define the telecoms sector. Only 10% of Communication Service Providers (CSPs) today feel comfortable introducing unsupervised, totally autonomous AI into live production environments.
About 78% of respondents would rather have partially automated or supervised solutions, keeping human operators in the loop.
Why the opposition? Three main obstacles emerged: a lack of in-house AI expertise to oversee these complex systems, the financial cost of upgrading infrastructure to fit advanced AI workloads, and most importantly, a strong worry about dependability and trust.
Telecom infrastructure is too important for systems to fail or make opaque decisions that humans cannot readily audit or undo; it is even sometimes life-saving. This emphasises a major drawback in the current drive for agentic artificial intelligence: operator confidence is not where the technology may be ready.
Ethical questions add still another level of complication. Should artificial intelligence be allowed to give some clients higher priority than others under bandwidth constraints? How can personalisation driven by artificial intelligence prevent aggravating digital inequality? The more decisions these systems make, the greater the demand for accountability mechanisms to monitor and explain them.
Will the leap be taken?
From AI-assisted systems to agentic designs, this is an ideological as much as a technical challenge. It challenges accepted wisdom regarding responsibility, control, and the part human knowledge can play in intricate systems. This leap forces carriers to fundamentally rethink operations, far more than a small step.
And there are significant hazards as well. Autonomous agents that misinterpret scenarios could cause operational interruptions, expose security flaws, or conflict with laws requiring human supervision. Add to this the increased scrutiny from authorities and watchdogs all throughout Europe, North America, and Asia, and it is evident why telcos are still reluctant to fast-track this change.
Still, small adoption could be the path ahead. Telcos could begin with hybrid systems, semi-agentic configurations whereby AI agents propose and carry out low-risk decisions under supervised observation. Before extending to mission-critical areas like core infrastructure or emergency communication layers, this hybrid approach provides a safe runway for internal familiarity and trust-building.
Over the next decade, industry analysts project that intelligent automation in telecoms could generate as much as $200 billion in value. Although only a fraction of this can be attributed to agentic artificial intelligence, its influence is projected to grow as systems develop and confidence increases.
Evolution, not revolution
Agentic artificial intelligence seems on paper to transform telecom operations. Real-world acceptance is probably not going to be sudden, though. Rather, the change will probably be seen as a slow evolution whereby companies add agentic elements on top of current systems instead of replacing them entirely.
Still, a change is in process. Over half (51%) of CSPs in another Telecoms.com survey said that artificial intelligence is starting to profoundly change their perspective on operations, customer interaction, and service delivery.
These figures indicate growing excitement mixed with institutional and cultural immobility. Particularly in layers of artificial intelligence operations and customer management, where agentic capabilities can generate quantifiable returns without compromising fundamental service dependability, investments are gradually rising.
Concurrently, telecom decisions on new technologies are being shaped by outside elements ranging from geopolitical concerns to inflation-driven cost management measures. Even if customer-facing installations remain delayed, the urge to safeguard systems through agentic techniques may ironically hasten adoption in some security roles in areas with weak data laws or dangerous cyber environments.
Agentic AI may find its way from network administration into strategic tasks, including infrastructure investment forecasts, market competitiveness response, or real-time price optimisation in the long run. Still, those use cases are only conceptual. For telecom companies, agentic artificial intelligence has great promise, offering a means to turn reactive systems into intelligent, proactive networks. But telecoms is a business based on dependability, consistency, and confidence.
Agentic systems may shine in demos and pilot environments, but to have a significant role in telco operations, it will demand a demonstrated track record of dependability and openness.
In the near term, one might expect a pragmatic approach: well-scoped pilot projects, semi-autonomous decision-making in controlled situations, and a consistent change in perspective from human-led to human-supervised AI.
The actual change will be incremental and consistent, not a flash revolution. Agentic artificial intelligence is still a fascinating frontier for now, and one operator will explore with one hand on the throttle and the other firmly on the brake.