From Automation to Agency: What Agentic AI Means for BSS

Introduction

Business Support Systems (BSS) have long relied on automation to reduce errors and improve efficiency. Yet these systems remain reactive: rules are rigid, workflows fragmented, and human oversight is needed at almost every step. Increasingly, BSS is expected to evolve into autonomous, adaptive platforms rather than transactional back-office engines.

This is where Agentic AI comes in. Often described as “goal-seeking systems composed of AI agents,” it represents a phase in which multiple agents can reason, act, and collaborate across systems. Product configuration and catalogue management industry pilots show this is no longer theory; early deployments are already underway.

Agentic AI is a network of “digital teammates” able to reason, act, and collaborate towards a goal. Unlike automation, which follows fixed rules, these agents adapt to context and complete multi-step tasks with minimal intervention.

For telecom BSS, this shift extends across familiar areas such as billing, catalogue, campaigns, customer care, and partner management, while also reaching into network operations, fraud detection, security response, and business intelligence. Instead of simply flagging issues, agents coordinate to resolve them end-to-end.

For example, one agent may identify a customer likely to churn, another design a retention offer, a third configure it in the catalogue, and a fourth trigger the campaign in real time. In parallel, agents could spot unusual usage patterns, isolate potential fraud, and alert teams before losses occur.
 
For operators, this means systems that self-adjust, enhance customer experience, and drive growth. For vendors, it highlights the need to design agent-ready, scalable, and governance-enabled platfroms.

Building on these capabilities, orchestration is the mechanism that makes multi-agent collaboration work reliably in practice.

Multi-Agent Orchestration: The Core Enabler

The real strength of agentic AI in BSS lies in orchestrating the control layer that plans work, coordinates hand-offs, manages errors, and records every action. In simple terms, it gives each agent a defined role, ensures tasks are carried out in the correct order, and adds approval steps where human oversight is needed.

A clear example is catalogue migration. In recent pilots, one agent read legacy offers, another mapped them to a new model, a third created and tested configurations, and a fourth prepared documentation. The orchestrator validated results and scheduled release, showing how complex processes can be automated end-to-end.

Operationally, orchestration also enables agents to spot unusual activity, quarantine accounts, trigger provisional blocks, and log a complete audit trail, all sequenced and controlled for compliance. Beyond efficiency, it provides operators with confidence that automated actions remain traceable and auditable.

For vendors, this means designing cloud-native, modular, API-first platforms that allow agent-driven workflows to scale safely across BSS domains.

While orchestration ensures coordination, governance frameworks provide the trust and transparency needed for operators to embrace these.

Frameworks and Governance: Building Trust in Agency

To work reliably in BSS, agentic AI  must operate within clear frameworks and governance structures. These create the boundaries that make agent decisions transparent, accountable, and compliant with telecom standards.

At the framework level, standards such as the Model Context Protocol (MCP) allow agents to connect securely to tools and data, while protocols for agent-to-agent communication ensure they coordinate tasks without conflict. Orchestration patterns involve agent planning, others executing, approval steps where oversight is needed and giving each agent a defined role within a controlled flow.

Governance adds trust. Observability makes every agent action visible; audit trails record decisions for compliance; and human-in-the-loop checkpoints oversee on high-impact changes. Equally important is data readiness: unified, high-quality data estates ensure agents act consistently and accurately.

For vendors, the priority is clear: design BSS platforms with governance built in from the start, combining standards, transparency, and observability so operators can adopt agentic workflows with confidence.

These foundations are not theoretical; early pilots and analyst commentary demonstrate that agentic AI is already being tested in live BSS environments.

Industry Signals: Proof It’s Already Happening

Agentic AI in BSS is no longer confined to research papers. Early trials demonstrate how agents can take on complex workflows. Live experiments have shown agents parsing legacy product data, mapping it to new models, generating configurations, and preparing release documentation, all orchestrated with minimal human input.

Other pilots are exploring multi-agent systems for automated product configuration, customer management, and billing adjustments. Analysts also highlight monetisation, fraud prevention, and customer experience as the first areas where agentic AI will deliver measurable benefits.

While these deployments remain at pilot scale, they signal a clear shift: BSS is entering its agentic chapter. The trajectory is evident: operators are testing, analysts are validating, and vendors must ensure platforms are ready for broader adoption.

Conclusion: Moving Towards Proactive BSS

The move from automation to agency signals a new chapter for BSS. Instead of reactive, rule-based systems, operators can expect platforms that anticipate, adapt, and act in real time. Early pilots already confirm the direction of travel, pointing towards resilience, faster launches, and sharper customer experiences.

At Covalense Digital, we prepare operators for this future with cloud-native, modular, and API-first BSS platforms designed for agentic adoption. Connect us at reachus@covalensedigital.com to start your journey towards proactive, agent-ready BSS.

 

Author

Shreya Chakravarthy, Associate Product Marketing Research 
Shreya is focused on translating market intelligence into competitive advantage. She drives strategic product positioning by performing in-depth market analysis and evaluating emerging trends. Her insights ensure product roadmaps are directly aligned with evolving customer and industry demands.