Industry benchmarks indicate that more than 70% of network outages stem from software defects, configuration errors, and operational complexity – not hardware failure – while the financial impact of a major outage frequently exceeds USD 300,000 per hour for large communication service providers and digital enterprises.

At the same time, the scale of networks and our dependence on them is accelerating. 5G standalone, cloud-native cores, edge computing, and AI workloads are driving exponential growth in telemetry, interdependencies, and real-time performance expectations.

In this environment, traditional monitoring and rule-based anomaly detection are no longer sufficient. What organizations now require is predictive, autonomous intelligence that anticipates failures, understands business impact, and enables action at machine speed. This shift is driving the emergence of a new generation of powerful anomaly detection applications that use agentic AI, designed to support the journey toward autonomous networks.

Why traditional anomaly detection falls short

Conventional anomaly detection approaches typically rely on centralized analytics engines, static thresholds, or isolated machine learning models. While effective for known failure patterns, these systems struggle with today’s realities: dynamic traffic patterns, multi-vendor environments, cloud-native network functions, and cross-domain dependencies that span RAN, core, transport, and IT.

The result is well known to operations leaders: alert storms, delayed root-cause analysis, and reactive interventions that occur after customer experience and revenue have already been impacted. As network complexity increases, simply scaling existing tools adds cost and noise – but not resilience.

The multi-agentic advantage

Multi-agentic anomaly detection represents a fundamental shift from isolated intelligence to collaborative, purpose-driven AI.

Instead of a single model attempting to interpret the entire network, multiple specialized AI agents operate autonomously, each with a defined role, context, and decision authority. With engineer provided data and inputs, these agents continuously collaborate, share insights, and adapt their behavior, based on network intent and outcomes.

Typical agent roles include:

  • Observation agents that continuously ingest and contextualize high-volume telemetry across network and cloud domains
  • Detection agents that identify subtle deviations and weak signals before service-impacting thresholds are crossed
  • Prediction agents that forecast failures and degradation by correlating anomalies with topology, history, and external context
  • Reasoning agents that analyze cause–effect relationships of predicted failures, evaluate their impact, and provide actionable, cross-layer and multi-vendor recommendations, leveraging network digital twins for deeper, context-aware insights
  • Action agents that recommend or execute corrective actions aligned with policy, risk, and business priorities

While agents resolve routine and chronic anomalies autonomously, human-in-the-loop oversight is embedded across all anomaly types. This ensures contextual validation and accountability, and is further elevated for complex or high-impact scenarios before actions are executed.

Through this intelligent human-AI collaboration, the system moves beyond anomaly detection to situational awareness, foresight, and decision support.

From alerts to business outcomes

The most significant value of a multi-agentic approach is not technical elegance, but measurable business impact. By predicting failures earlier and prioritizing issues based on service and customer impact, organizations can significantly reduce mean time to resolution, avoid revenue loss, and improve SLA compliance.

  • For CSPs, this enables higher network availability, lower operational expenditure, and greater confidence in supporting latency-sensitive services, like network slicing, private 5G, and edge AI
  • For NEPs, multi-agentic intelligence enhances product differentiation, by embedding advanced assurance and diagnostic capabilities directly into network platforms
  • For enterprises, it delivers resilient digital infrastructure that supports mission-critical operations, without constant manual intervention

Operating complex networks at scale

For CSPs, NEPs, and enterprises, multi-agentic anomaly detection is not simply the next evolution of monitoring – it is a strategic capability for operating complex networks at scale. By shifting from reactive operations to predictive, intent-driven intelligence, organizations can transform network resilience from an operational challenge into a sustained competitive advantage.

The value of a well implemented and deployed multi-agentic anomaly detection is tangible and measurable :

  • Reduced revenue and brand risk, through early detection and prevention of service-impacting incidents
  • Lower operational costs, by shifting from manual, reactive troubleshooting to autonomous, closed-loop operations and proactive maintenance
  • Faster time to insight and action, enabling operations teams to focus on optimization, not firefighting
  • Improved customer experience and SLA performance, especially for high-value enterprise and 5G use cases
  • Enhanced operator trust and faster adoption, through clear, explainable AI insights and transparent decision-making
  • A scalable foundation for autonomous networks, through distributed  with multi-agentic intelligence, future-proofing the organization, as complexity and demand continue to rise

In practical terms, this means fewer major outages, shorter recovery cycles, and a network that increasingly operates with intent, resilience, and confidence at machine speed.

The time to act is now

Now is the time for CSPs, NEPs, and enterprises to move beyond experimentation and embed agentic AI into the core of network operations.

Those who act decisively will not only reduce risk and cost – but also position their networks as strategic assets, capable of supporting AI-driven growth, new services, and differentiated customer experiences.

The question is no longer whether networks should become autonomous, but how quickly can your organization turn intelligence into sustained business advantage?

Join us at Mobile World Congress 2026 in Barcelona at the Capgemini booth, Hall 2K21, to experience these capabilities firsthand.