Artificial intelligence has reached a new inflection point for communications service providers (CSPs). After years of using machine learning for tasks such as anomaly detection, predictive maintenance and network optimization, the industry has now entered the era of Agentic AI.
Unlike traditional AI systems that simply generate responses to prompts, Agentic AI introduces intelligent software agents that can reason, plan, collaborate and take autonomous action. These agents can coordinate complex workflows, interact with enterprise systems and orchestrate specialized AI models, creating a practical pathway towards autonomous networks capable of monitoring, diagnosing and resolving issues with minimal human intervention.
However, while large language models (LLMs) have captured much of the industry’s attention, their success depends on one critical factor that is often overlooked: data quality. Without a trusted, telecom-specific data foundation, even the most advanced AI agents cannot deliver reliable outcomes.
From AI assistants to AI operators
The first wave of enterprise AI focused on improving employee productivity through tools that summarized information, generated content or accelerated software development. Humans remained responsible for making decisions.
Agentic AI changes this model. Instead of responding to prompts, AI agents perform multi-step tasks independently, retrieve information from multiple systems, collaborate with other agents and execute predefined actions. In telecommunications, this means moving beyond digital assistants towards intelligent operators capable of managing increasingly complex network operations.
This shift comes at a crucial time. As CSPs invest heavily in 5G and prepare for 6G, network complexity continues to increase while operational resources remain constrained. Manual investigations, siloed teams and reactive troubleshooting are no longer sustainable. Future telecom operations will rely on intelligent systems that can understand network behaviour, identify problems, coordinate investigations across multiple domains and initiate remediation far faster than traditional approaches.
The advantage and challenges of AI in telecoms
Telecom networks generate enormous volumes of operational data every day, including performance metrics, alarms, customer experience data, configuration changes and service quality indicators.
When used effectively, this data allows AI to detect anomalies before customers notice them, predict capacity demands, optimize energy consumption, automate root cause analysis and improve customer experience.
Yet despite this opportunity, most telecom data is not ready for autonomous operations.
Operational information is often fragmented across OSS and BSS platforms, vendor-specific systems and separate network domains. Different applications use inconsistent data models, naming conventions and identifiers, making meaningful correlation difficult. Engineers frequently spend more time collecting and reconciling data than analysing it.
For AI, fragmented data creates an even bigger problem. LLMs perform well when working with accurate, structured information, but incomplete or inconsistent datasets increase the risk of inaccurate conclusions and hallucinations—an unacceptable outcome for mission-critical networks.
Building a trusted telecom data foundation
Simply collecting more data is not enough. Telecom information must be cleansed, standardized, enriched and correlated before it can support autonomous decision-making.
While many organizations begin with enterprise data lakes, raw data alone provides limited value. The real transformation occurs when operators create trusted telecom data products by:
- Cleaning and validating operational data
- Standardizing formats and identifiers
- Correlating information across multiple domains
- Mapping data into common telecom-specific models
- Enriching datasets with AI-generated insights such as anomaly detection, forecasting and root cause analysis
Presenting AI with curated, context-rich information dramatically improves both accuracy and reliability while reducing misleading outputs. Rather than asking LLMs to interpret vast amounts of raw operational data, organizations provide them with trusted knowledge engineered specifically for telecom operations.
Telecom expertise remains essential
Choosing the latest foundation model is only one part of the equation.
General-purpose AI models understand language exceptionally well, but they do not inherently understand radio access networks, service assurance, OSS architectures, network topology or telecom operational workflows.
Successful Agentic AI therefore depends on combining advances in AI with decades of telecoms expertise. Existing investments in service assurance, machine learning and automation remain valuable. Agentic AI builds upon these capabilities by orchestrating them through intelligent, collaborative agents rather than replacing them entirely.
The journey towards autonomous networks is therefore not simply an AI initiative—it is a long-term data transformation programme supported by telecom expertise and operational maturity.
Multi-agentic AI for service assurance transformation
ne of Agentic AI’s greatest strengths is its ability to coordinate teams of specialized AI agents.
In a telecom environment, different agents may monitor network performance, analyse faults, investigate customer experience, interrogate configuration systems or validate changes against operational policies. A supervisory agent coordinates these specialists, creating a unified investigation rather than relying on disconnected manual processes.
This approach mirrors how experienced engineering teams collaborate today—but at machine speed.
Service assurance is one of the most promising applications. Instead of passing incidents between multiple operational teams, AI agents can automatically reconstruct subscriber sessions, analyse radio conditions, correlate fault and performance data, review historical trends and validate recent configuration changes.
The outcome is evidence-based root cause analysis delivered rapidly to engineers, while many routine corrective actions can be recommended or executed automatically.
Putting customer experience at the centre
Agentic AI also enables CSPs to shift from network-centric operations towards customer-centric operations.
Historically, customer service teams and network operations have worked largely independently. AI agents bridge this gap by combining subscriber experience, network telemetry, service assurance and geolocation data to identify not only that an issue exists, but exactly which customers are affected and how severely.
This enables CSPs to detect issues earlier, communicate proactively and prioritize remediation based on customer impact rather than infrastructure metrics alone. Over time, predictive customer experience management could allow operators to resolve problems before customers even notice them.
A practical roadmap
Autonomous networks will not be achieved through a single AI platform or overnight transformation. Success requires collaboration between CSPs, technology providers and cloud partners, while protecting existing OSS investments through open integration and orchestration.
The most effective approach is incremental: begin with high-value operational use cases, establish a trusted telecom data foundation, expand automation gradually and introduce specialized AI agents where they deliver measurable business value. Throughout this journey, governance, transparency and human oversight remain essential as organizations build confidence in increasingly autonomous operations.
The future starts with trusted data
Agentic AI represents one of the most significant opportunities the telecom industry has seen in decades. It has the potential to transform network operations, improve customer experience and accelerate the journey towards autonomous networks.
But success will not be determined by who deploys the largest language models or the greatest number of AI agents. It will belong to the organizations that invest first in trusted telecom data, combine AI with deep domain expertise and adopt a pragmatic, business-led approach to automation.
Autonomous networks will be built on millions of trusted data points, intelligent collaboration between specialized AI agents and a clear operational vision that keeps customer experience at the centre of every decision.
This thought leadership blog is an AI-supported summary of Mycom’s webinar on the topic: Building an Agentic AI Data Foundation for Telcos. The speakers at this webinar were experts from Pipeline, AWS, Groundhog and Mycom.
To see the full recording of the webinar, click here.
