Telecommunications Billing System Modernization Automation

Decomposing a monolithic telecom billing system (e.g., legacy Oracle BRM or custom C/C++ solutions) is a high-stakes engineering project. Manual analysis and refactoring of rating, charging, and invoicing logic is slow, error-prone, and jeopardizes revenue assurance. A custom automation workflow directly targets this bottleneck, parsing millions of lines of undocumented code to map business rules, identify bounded contexts for microservices, and generate validated target code. This reduces migration planning from years to months and de-risks the core financial engine of the operator.

Implementation requires orchestrating specialized agents for code parsing, graph analysis, and test generation, integrated with version control and CI/CD pipelines. The workflow must include governance gates where architects validate service boundaries and business users sign off on extracted logic. The output is a suite of containerized services—rating engine, usage aggregator, invoice composer—with built-in observability and the ability to integrate with new digital partner APIs. This creates a phased, controlled migration path that protects ongoing revenue while modernizing the stack.

The core operational bottleneck is the manual, error-prone process of deciphering undocumented business logic within legacy BRM or custom billing monoliths. A custom multi-agent pipeline automates this discovery and translation, directly reducing migration planning from quarters to weeks and eliminating the risk of losing critical revenue logic. The financial upside comes from accelerated decommissioning of expensive mainframe infrastructure and the creation of a scalable, API-first billing core that supports new digital services and partnerships.

Implementation requires orchestrating specialized agents—built on frameworks like LangGraph—that interface directly with source code repositories, mainframe terminals, and data extracts. The pipeline integrates validation checkpoints where generated services are benchmarked for functional parity and performance against the legacy system. Critical controls include automated reconciliation of test transaction outcomes, human-in-the-loop approval for high-risk logic changes, and comprehensive observability into each agent's decisions to ensure auditability throughout the phased cutover to the new architecture.

Modernization begins with an automated discovery agent that inventories the monolithic codebase, mapping all rating, charging, and invoicing logic. This creates a system-of-record for the migration, identifying high-risk regulatory components and inter-service dependencies. The output is a prioritized decomposition roadmap, quantifying technical debt and forecasting effort, which becomes the single source of truth for all downstream translation and validation phases, ensuring no critical business rule is lost.

Execution follows the gated roadmap. Translation agents, specialized for COBOL or BRM logic, generate target microservices with embedded unit tests. Each service undergoes automated integration testing against legacy transaction flows. A parallel run phase uses synthetic and mirrored live traffic to validate functional equivalence and performance SLAs. Governance is enforced via automated change-approval workflows and real-time reconciliation agents that flag revenue discrepancies, allowing for rollback of individual services before full legacy decommissioning.

A robust governance layer is non-negotiable for billing modernization. The automation workflow must embed controls at each phase: logic extraction validation, parallel run validation, and post-cutover reconciliation. This is enforced through automated approval gates that require sign-off from revenue assurance and architecture teams before any translated service progresses. The system must generate immutable audit logs of all translation decisions and logic mappings to support future audits and dispute resolution, directly protecting the operator's revenue stream.

Phased rollout is executed via a traffic orchestration layer, likely built on service mesh (Istio, Linkerd) and API gateways. Phase 1 deploys non-critical rating logic with shadow traffic. Phase 2 runs new charging services in parallel with the legacy system, with automated daily reconciliation of billed amounts. Phase 3 implements canary releases for core invoicing modules, routing a small percentage of live traffic based on customer segment. Rollback triggers are automated based on key metrics like invoice discrepancy rates or processing latency, ensuring any anomaly halts progression and triggers investigation.