Multi-Agentic Anomaly Detection Application (MADA) uses agentic AI and collaborative autonomous agents to predict, diagnose, and remediate network issues, enabling resilient, self-healing, and increasingly autonomous telecom operations. 

Telecom networks are becoming increasingly intelligent and complex, driven by AI-RAN, cloud-native architectures, Open RAN, edge computing, and autonomous services. Traditional operations can no longer keep pace with growing event volumes and operational complexity.  

Capgemini’s Multi-Agentic Anomaly Detection Application (MADA) applies agentic AI through a decentralized swarm of specialized AI agents that collaboratively perceive network conditions, predict anomalies, perform impact analysis, recommend corrective actions, and autonomously execute remediation. The result is faster issue resolution, improved service reliability, reduced operational costs, and a clear path toward AI-native autonomous network operations. 

“Autonomous networks require more than intelligent automation. They require intelligent agents that collaborate, learn, adapt, and continuously improve network operations while keeping humans in control where it matters.”  

Subhankar Pal

Communication service providers are operating networks that generate unprecedented volumes of telemetry, events, and operational data. As AI workloads, AI-RAN, cloud-native infrastructure, Open RAN, edge computing, and network slicing become mainstream, traditional operations based on dashboards, rule-based automation, and manual troubleshooting are no longer sufficient. Operators need intelligent systems capable of making decisions collaboratively and acting autonomously. 

Capgemini’s Multi-Agentic Anomaly Detection Application (MADA) brings agentic AI into network operations through a decentralized multi-agent swarm architecture. Rather than relying on a centralized orchestrator, MADA distributes intelligence across specialized agents that collaborate dynamically to solve operational challenges: 

  • Dispatcher agents route requests across the agent ecosystem 
  • Perceive agents continuously detect anomalies 
  • Plan agents correlate events using insights from the Network Digital Twin to predict failures and perform impact analysis 
  • Act agents execute closed-loop remediation 
  • Reflect agents continuously evaluate outcomes to improve future decisions. 

This collaborative intelligence enables networks to transition from reactive incident management to predictive, adaptive, and increasingly self-healing operations. MADA not only detects problems before they impact customers but also recommends and autonomously executes corrective actions while maintaining governance and human oversight when required. 

Built on open agent communication standards including Agent-to-Agent (A2A), Agent-to-Human (A2H), and Model Context Protocol (MCP), MADA enables interoperable AI collaboration across telecom ecosystems. 

By reducing mean time to detect and resolve incidents, improving SLA compliance, lowering operational expenditure, and enhancing customer experience, MADA provides communication service providers with a scalable foundation for AI-native, autonomous network operations. 

Use case: Cyber Twin

The Cyber Twin solution showcases MADA in action. Recognized through the TM Forum Catalyst program, Cyber Twin provides a semantic digital twin that combines network topology, service relationships, and cybersecurity data into a unified knowledge graph, providing a single source of truth for network and cyber operations. Leveraging this unified knowledge graph, Network Digital Twin technology, and collaborative agentic AI, MADA delivers predictive insights, autonomous decision-making, and closed-loop automation. This supports faster fault resolution, improved network resilience, and a practical pat