Multi-Agent Automation of Enterprise RAN-as-a-Service (RANaaS) Provisioning

Manual RAN provisioning involves sequential handoffs between sales, design, engineering, and operations teams, often taking 4-6 weeks. A custom agentic workflow orchestrates OSS/BSS integration, automatically translating a sales order into validated network designs, resource checks, and configuration pushes. This reduces the service delivery cycle by over 95%, enabling same-day activation and creating a competitive advantage in enterprise sales.

Manual processes for site integration, slice configuration, and policy setup are labor-intensive and error-prone, leading to costly rework and SLA breaches. Autonomous agents execute standardized, auditable playbooks across inventory, SDN controllers, and policy engines (PCRF/PCF). This eliminates ~70% of manual touchpoints, reducing operational expenses and improving first-time-right provisioning quality for complex B2B slices.

Static, over-provisioned RAN capacity represents sunk capital with low utilization. This workflow productizes that capacity, enabling dynamic, policy-governed allocation of spectrum, compute, and radio resources to enterprise customers via a self-service portal. It creates a new, high-margin revenue stream by turning fixed network cost into variable, usage-based service income, directly improving asset ROI.

Manual processes don't scale. Adding 100 new enterprise slices would overwhelm network operations. This automated architecture uses a coordinator agent and specialized design, inventory, and assurance agents to manage the entire lifecycle in parallel. It enables zero-touch scaling of the RANaaS product without linearly increasing headcount, protecting margins as the customer base grows.

Reactive troubleshooting after an SLA breach damages customer relationships and incurs credits. The workflow integrates continuous monitoring agents that track slice performance against contracted KPIs. Upon predicting a breach (e.g., latency spike), agents automatically trigger remediation—reallocating resources, adjusting QoS policies—to defend SLAs proactively, reducing breach penalties and strengthening retention.

Building this custom orchestration layer (using frameworks like LangGraph for agent coordination) creates a reusable platform. The same agentic architecture and integrations can be extended to manage other network-as-a-service offerings (e.g., security, edge compute), future-proofing operations and accelerating time-to-market for new services, thereby spreading development cost across multiple revenue lines.

This agent ingests the service order from the BSS or customer portal via API. It validates technical feasibility (e.g., location, spectrum availability) and commercial terms against policy engines and inventory systems. It creates the initial service blueprint and triggers the design phase, eliminating manual order desk review and reducing entry errors that cause downstream delays.

Using the validated order, this agent queries RF planning tools, digital twins, and resource inventories to design the optimal RAN slice. It determines cell parameters, computes required vDU/vCU resources, reserves spectrum, and defines QoS policies. The design is output as a machine-readable manifest, replacing weeks of manual network engineering work with minutes of automated simulation and configuration generation.

The core orchestrator executes the design manifest across fragmented OSS systems. It sequences API calls to: activate spectrum in the SAS, instantiate VNFs on the cloud infrastructure (Kubernetes/VIM), push configurations to RAN controllers (RIC, CU/DU), and update core network elements (NSSF, PCF). It manages dependencies and handles rollback on failure, enabling true zero-touch provisioning.

Once provisioned, this agent assumes continuous control. It ingests real-time KPIs from the RAN, transport, and core to verify SLA compliance. It uses predictive models to detect degradation and can trigger the Orchestrator for auto-remediation (e.g., scaling resources). It also generates assurance reports for the customer portal, shifting operations from reactive firefighting to proactive guarantee management.

This agent meters detailed resource consumption (compute core-hours, GHz-MHz of spectrum, energy) per slice and enterprise customer. It formats this into granular billing records and feeds the BSS/charging system (e.g., SAP, Oracle). It enables usage-based pricing models for RANaaS and performs continuous reconciliation to prevent revenue leakage, a critical capability for monetizing dynamic network slicing.

Not an agent, but the critical control plane interface. A secure API gateway exposes order status, real-time performance dashboards (from the Assurance Agent), and limited self-service controls (e.g., temporary bandwidth boost) to the enterprise customer's systems. This layer transforms the technical provisioning workflow into a sellable, transparent service product, directly driving customer satisfaction and retention.

The workflow is triggered by a validated order from the BSS (e.g., Salesforce, custom portal). An Order Intake Agent parses the service-level agreement (SLA), technical requirements, and commercial terms. It validates against product catalogs and credit systems, then creates a structured service design ticket. This integration eliminates manual data entry, reduces order-to-design cycle time from days to hours, and ensures the RAN slice specification is machine-readable for downstream systems.

A Resource Discovery & Design Agent queries federated OSS and inventory systems (e.g., Nokia NSP, Ericsson ENM, custom databases) to identify available spectrum blocks, compute capacity at target edge locations, and compatible radio hardware. It resolves dependencies and generates a concrete, conflict-free provisioning plan. This integration is critical for moving from a theoretical design to an executable one, preventing over-subscription and ensuring the service can be physically instantiated.

The approved design is passed to a central Orchestration Agent (e.g., built on Kubernetes operators, Ansible, or a custom orchestrator). This agent decomposes the plan into atomic tasks: VM/container instantiation on the edge cloud, configuration of virtualized Centralized/Distributed Units (CU/DU), and policy push to the RAN Intelligent Controller (RIC). It manages the stateful workflow, handles rollback on failure, and confirms each step. This integration automates the technically complex, multi-vendor provisioning sequence that is typically manual and error-prone.

Concurrently, a Core Provisioning Agent interfaces with the 5G Core's Network Slice Selection Function (NSSF) and Policy Control Function (PCF) via standard APIs (e.g., Nnssf, Npcf). It provisions the corresponding network slice identifier (S-NSSAI), establishes subscriber profiles, and sets QoS policies that align with the RAN slice. This bidirectional integration ensures end-to-end service consistency and is mandatory for delivering the guaranteed SLA promised in the B2B contract.

Upon successful activation, a Service Assurance Agent registers the new slice with the performance management and data analytics systems. It establishes KPI baselines and alerting rules. Simultaneously, a Metering Agent begins collecting granular resource consumption data (compute-hours, spectrum-MHz) and streams it to the billing/mediation system (e.g., AMDOCS, custom). This integration closes the business loop, enabling usage-based charging, automated SLA reporting, and creating the data foundation for predictive optimization.

A Supervision & Escalation Agent monitors the entire workflow via a shared observability bus. It catches exceptions (e.g., inventory conflict, API timeout, KPI violation), classifies them, and routes them to predefined human queues in ITSM tools (e.g., ServiceNow, Jira) with enriched context and suggested remediations. This integration provides the essential control layer, ensuring the automated system can fail gracefully and operators are only engaged for non-routine decisions, preserving the low-touch economic model.