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Troubleshooting with Agentic AI

  • Autonomous anomaly detection: Early identification of network deviations based on AI-driven comparison of real-time and expected parameters
  • Awareness and analysis: Automated root-cause analysis, gathering the full context of an incident or detected anomaly from available logs, TMF open APIs, and domain agents
  • Control loop management: Secure the most sensitive part — the decision-making — based on complete insight and trusted data. Enable execution with a “human-in-the-loop” option when required, or in cases of missing expert knowledge, trusted data, or a viable solution
  • Closed loop control: Real-time execution, validation, and enforcement of network changes triggered by business intents and agent outputs
  • Autonomous field dispatch: Task planning and coordination for human interventions requiring physical access, with task completion confirmed by automated control loops. planning and coordination for human interventions requiring physical access, with the confirmation of task completion handled by automated control loops
  • Dark NOC monitoring: Autonomous network supervision with proactive remediation and minimal human involvement. Provides complete traceability, observability, and documentation for every step of the awareness, analysis, decision, and execution phases

  • Continuous knowledge-building: AI-generated suggestions based on supplier documentation, historical data, and service performance models, with self-learning from planned and executed cases, as well as from expert knowledge stored as text
  • Control loop feedback: Continuous learning from optimization outcomes to enrich AI decision-making and expand the internal knowledge base

Discover how agentic AI takes you to level 4: Schedule your demo now 

End-to-End Order-to-Cash Powered by Intent

  • Analysis of customer intent: Using an LLM for automated interpretation of new service requests to build awareness of the goal and assess the impact on configuration changes based on network-as-a-service models
  • Intent-based task structuring: Generation of plans, request pipelines, and control loops for agents and API calls
  • Cross-domain orchestration: Coordination and execution of conversational confirmations (for scope and quotations) and service delivery processes across technical, operational, and partner domains
  • Intent-based fulfillment: Autonomous service activation — including testing, monitoring, and billing — through standardized APIs and defined control loops
  • Inventory synchronization: Real-time updates to inventory systems reflecting service deployment and configuration status, with a
    digital twin function to detect potential collisions before changes are uploaded to the network
  • Stakeholder communication: Automatic notification of service launches via APIs, delivered in a human-readable format across internal systems and external partner environments

Ready to transform with intent-driven operations and advanced analytics? Schedule your demo now

Multi-Criteria Network Optimization

  • Demand pattern analysis: Identification of service usage trends based on time of day, week, season, and other influencing factors
  • Load fluctuation detection: Recognition of recurring, high-impact performance shifts across virtual and physical network elements
  • Intent-based resource reallocation: Intellingent na multi-criteria optimization of service delivery through alternative resources to reduce cost, emissions, and latency
  • Intelligent resource consolidation: Evaluation and merging of underutilized assets while maintaining performance and SLA compliance
  • Capacity reclaim and reuse: Repurposing freed resources to support new services or deeper network consolidation initiatives
  • Scheduled auto-configuration: Timed, policy-driven configuration changes triggered by predictable seasonal or usage-based variations.
  • Closed loop execution: Autonomous implementation of optimization tasks via atomic orders and automated system updates.
  • Control loop feedback: Continuous learning from optimization outcomes, enriching AI decision-making and the internal knowledge base

MIRA by Comarch powered innovation in a TM Forum Catalyst project

Private 5G Enabled by Autonomous Networks

  • Automatic intent translation: Converting customer vertical-specific intents into network design and technical intents
  • Dark NOC operations: Eliminating the need for additional experts to manage private 5G networks at scale
  • Private 5G for SMEs: Autonomous, intent-driven orchestration tailored even for small businesses
  • Agentic AI enablement: Learning vertical-specific needs to bridge the knowledge gap between industries and network operations
  • End-to-end toolchain: From customer engagement and IoT Connect to network operations management
  • Open RAN adoption: rApp/xApp intelligence enhancing RAN performance to meet SME requirements
  • MEC intelligence: AI on the edge powering vertical-specific applications

Visit Comarch’s 5G Lab to see autonomous networks in action

Trusted Partner for Level 4 Autonomy

Comarch cloud native multi agentic AI platform for autonomous network operations enables efficiency, resilience, and agility. It integrates seamlessly with existing ecosystems and supports flexible deployment models. The solution builds on Comarch’s proven expertise in BSS/OSS/IoT connectivity and network automation, extended with AI-driven orchestration to meet the evolving demands of next-generation, highly automated service environments.

Intent-based

Define intents in natural language or via TMF open API requests, and use an intent-driven control loop.

Integration of all available knowledge

Incorporate trusted external data, including weather conditions or social events, into your analytics and forecasting.

Collaboration between AI agents

Analyze how cooperating AI agents make decisions and exchange information using protocols like MCP and A2A, as well as standard industry APIs.

Human-in-the-loop (as needed)

Pause automated processes to allow for operator intervention at any stage, or if agents cannot agree on a trusted decision or plan.

Knowledge, data integrity, and interference resistance

Implement mechanisms that filter and validate information to protect against false or malicious external inputs.

Standardization of historical records

Convert human-recorded historical events into a standardized format for automated systems.

Control loop management

Use a digital twin to simulate rare or crisis scenarios to train the system and test responses.

Agile AI integration

Rapidly add individual AI agents or entire teams to extend your capabilities into new domains and processes.

Benefits

Autonomous service operations at scale

End-to-end automation ensures consistent, policy-driven service delivery with minimal
human intervention across distributed networks. Building high trust using scenarios for big
companies

Continuous optimization for performance and cost

AI-powered insights and adaptive configuration changes enable real-time performance
tuning and operational cost savings.

Predictive assurance and service resilience

Early anomaly detection, forecasting and self-healing mechanisms maintain high service
availability and prevent disruptions before they occur.

Faster time-to-value for new services

Zero-touch order management and dynamic orchestration accelerate service activation
across both internal and partner ecosystems.

Smarter resource utilization and energy efficiency

Automated resource scaling and consolidation based on demand patterns support
sustainability goals mainlyny in 5G/5G SA and reduce infrastructure waste.

Future-proof path to full network autonomy (L4/L5)

A modular, intent-driven architecture lays the foundation for evolving toward full network
autonomy in line with TM Forum standards.

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