Lumen is deploying Ciena’s Blue Planet AI Studio across its network‑operations backbone, introducing a suite of AI agents that automate device‑model creation and digital‑network‑twin updates. By feeding real‑time OSS data into low‑code agents, Lumen aims to slash manual configuration cycles, free engineers for higher‑value work, and move AI from pilot to daily operation.
Why Lumen Is Turning to AI‑Driven Automation
Lumen runs one of the nation’s most intricate fiber and edge‑computing networks, and the sheer scale makes manual processes costly and error‑prone. The company needed a coordinated ecosystem of agents that can talk to each other across domains, not just a single AI model. That’s why the low‑code, OSS‑native platform fits its roadmap.
Low‑Code, OSS‑Native Platform
Blue Planet’s AI Studio offers an open, multi‑agent environment built directly into the operational support system. You can deploy pre‑crafted agents, plug in third‑party solutions, or build custom ones with a visual low‑code builder. The framework follows open standards, ensuring that agents stay interoperable as the network evolves.
Key Use Cases: Device Models and Digital Twins
The first rollout focuses on two high‑friction tasks: generating accurate device models and maintaining digital twins of the network. These chores usually consume weeks of engineering time, but AI agents can ingest live OSS data and produce updates in minutes. The result is a more responsive network that mirrors reality in near real‑time.
How the AI Agents Work Within Lumen’s OSS
Agents pull structured data straight from OSS APIs, eliminating the bulky data staging layers that slow down generic AI platforms. This direct line reduces latency and improves decision fidelity, so the system can react to changes faster than before.
Direct API Integration Cuts Latency
Because the agents talk to the same APIs that engineers use daily, you’ll notice quicker feedback loops. The platform also logs every action, making it easy to audit and troubleshoot any unexpected behavior.
Empowering Engineers Without Deep ML Skills
Thanks to the low‑code builder, network specialists can translate their domain knowledge into reusable agents without writing complex machine‑learning code. It’s a shift from “we need data scientists” to “you can create the automation you need today.”
What This Means for Network Operations Teams
Automation promises to free engineers from repetitive inventory reconciliation, configuration drift, and topology mapping. With those tasks offloaded, teams can focus on service innovation, like dynamic provisioning or real‑time QoS adjustments. The overall impact is higher efficiency and lower risk of human error.
- Reduced manual configuration cycles
- Faster device‑model generation and twin updates
- Foundation for future fault management and capacity planning automation
Next Steps and Future Expansion
The rollout follows a phased approach. Early pilots will validate device‑model and twin use cases, then the agent ecosystem will expand to cover fault detection, capacity planning, and service assurance. Third‑party developers are also invited to contribute specialized agents, enriching the marketplace.
Practitioner Insight
“We’ve seen the bottleneck in device‑model updates for months. If the AI agents can generate accurate models from live OSS data, it will shave days off our rollout cycles,” says Sarah Patel, senior network engineer at Lumen.
