Agentic AI in telecom: Why 2026 will be the breakout year and how telcos can actually get there
By Udai Kanukolanu
December 22, 2025
For years, telecom operators have talked about AI with a mix of hope and caution. It promised efficiency, smarter networks, fewer outages, and happier customers, but in practice, most AI projects stayed on the fringes. They automated small tasks, predicted a handful of events, or assisted call center teams, yet they rarely changed how networks or operations fundamentally worked.
Between 2024 and 2025, that picture shifted dramatically. A new class of systems called agentic AI began moving from research labs into live telecom environments. Unlike traditional AI, which predicts or recommends, agentic AI understands goals, plans actions, executes across systems, and learns continuously. For a domain as distributed, complex, and time-sensitive as telecom, this shift is foundational.
Across global enterprises, agentic AI is moving rapidly from experimentation to execution. A growing share of companies have already put agents into production, not just pilots, and most expect to scale them further. Organizations increasingly view agentic AI not only as an efficiency lever but as a path to new revenue streams and reimagined operations. What was once an early-stage experiment is now becoming a mainstream strategic priority.
Yet even as industry-wide adoption grows, telecom operators are deploying agentic AI in a controlled and heavily supervised manner. Agents today operate under strict guardrails: they can detect faults, reroute traffic, restart failing elements, or trigger basic remediation steps, but any impactful action, such as changing radio parameters, modifying live configurations, or initiating site-level maintenance, still requires explicit human approval. Customer-facing workflows allow agents to resolve common issues automatically, while billing, identity, or policy-sensitive actions remain human-controlled. Even in field operations, agents can diagnose and recommend fixes, but technicians still confirm execution.
In summary, most operators allow autonomy only in low-risk zones, with humans overseeing every decision that carries real operational consequences. The industry is clearly moving toward deeper autonomy, but today’s deployments remain intentionally conservative, balancing innovation with safety, predictability, and regulatory trust.
What agentic AI has already changed
The earliest progress came from customer operations. Voice agents that sounded clunky and robotic just a year ago can now hold more natural conversations. Digital agents are evolving beyond chatbots into execution engines that can troubleshoot simple issues, update subscription plans, generate workflows, and close loops without human assistance.
Network and field operations are not far behind. Agents can already detect congestion patterns, tune RAN parameters, trigger small cell activation, adjust power levels, and resolve certain issues autonomously. Field teams benefit from automated diagnostics and smarter ticket triage. IT operations are seeing agent-driven patching, scaling, and resource optimization. The industry has also strengthened its governance backbone: identity layers, auditability, and policy-restricted autonomy are now common in early deployments.
These early wins have set the stage for what is likely to come next, but it’s important to note how far away still we are from the finish line.
Key agentic AI trends reshaping telecom
A lot is changing, but a few agentic AI shifts stand out for how quickly they’re moving from pilots to real-world impact.
Collaborative multi-agent ecosystems across domains
The biggest shift is the rise of interconnected agents coordinating network operations, customer care, billing, security, and service assurance. These systems work toward shared intent, enabling telecom environments to behave more like holistic and autonomous digital ecosystems.
Self-optimizing and self-healing networks
Instead of engineers chasing alarms, agents now identify congestion, adjust parameters, reroute traffic, and resolve issues before customers ever notice – bringing true closed-loop automation closer to reality.
Autonomous customer experience journeys
Customer interactions are becoming end-to-end agent-driven, as we discussed previously.
Real-time fraud and security intelligence
Security agents continuously detect anomalies, block suspicious behavior, and work with network and CX agents to mitigate risks instantly, reducing both fraud losses and response times.
Agentic OSS/BSS automation and intent-driven service orchestration
OSS and BSS are becoming far more dynamic, with agents handling service provisioning, order fallout, policy enforcement, charging logic, and workflow generation. This shifts telcos from reactive back-office processes to real-time, intent-driven operations.
What 2026 looks like to me
2026 will move the industry a step closer to true autonomy, with networks becoming more self-managing and intelligent across domains. We will see coherent, multi-agent systems operating across domains in ways that meaningfully reduce operational friction. For example, telecom NOCs will begin to resemble multi-agent NOCs, where specialized agents detect, diagnose, orchestrate, execute, and verify tasks in parallel.
Across RAN, transport, and core, near-real-time optimization will become far more achievable, especially as cloud-native architectures and open APIs become standard. Customer experience will become more predictive, with agents identifying QoE degradation or churn risk before users feel the impact. Enterprise connectivity (think SD-WAN, private 5G, cloud integration) will shift toward self-adjusting systems. And more operational artifacts, like runbooks, remediation workflows, and dashboards, will be generated by agents.
Key aspects of this breakout will include:
Market growth: The global agentic AI market is projected to grow substantially around the 2026 timeframe, indicating a large-scale market transition.
Shift from "AI as a helper" to "AI as a doer": Agentic AI systems will move beyond simply providing insights or following instructions to autonomously observing, deciding, and acting within defined guardrails – at least in newer, select areas.
Massive scale and integration: Rather than isolated use cases, agentic AI will be embedded across all business functions. It will be integrated into core enterprise platforms and workflows.
Autonomous operations: Networks are expected to achieve the next level of self-optimizing. AI agents will manage traffic dynamically, predicting and preventing failures, and resolving issues in real-time.
Tangible business value: Telcos will make demands for concrete, measurable ROI, such as significant cost reductions and new revenue streams from AI.
New human-AI workforce model: The integration will necessitate new organizational structures and roles, with human employees and AI agents working as "hybrid teams" or "digital teammates”.
Trust and governance maturity: The increased autonomy will force the industry to prioritize and implement robust governance, transparency, and ethical frameworks to build trust and ensure end-accountability.
None of this would be possible without the groundwork laid over the last five years. Disaggregated networks, cloud-native platforms, automation-first design, and orchestration layers have created the programmable environment agentic AI needs.
Interestingly, Rakuten Symphony demonstrated automation-led, cloud-native operations at scale and helped prove these foundations even before agentic AI entered the spotlight.
To wrap up the first segment of my two-part blog – where does all this leave us? We certainly won’t disappear. My bet is: humans will simply move into supervisory and policy-defining roles instead of coordinating every task manually.
In 2026, what telcos need is not hype, but a structured path to deploy agentic AI safely and at scale. That’s what I’ll look to explore in Part 2.
symphony.rakuten.com/blog/...ow-telcos-can-actually-get-there
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