Multi-Agent Automation of Network-Wide Energy Consumption

This workflow directly targets the 20-40% of network OPEX consumed by energy. It automates the manual, reactive tuning of cell site power states—a bottleneck that leaves savings unrealized during low-traffic periods. By deploying coordinating agents that ingest live traffic, weather, grid carbon intensity, and hardware telemetry, the system makes continuous trade-off decisions between energy draw and SLA compliance. Savings accrue from reduced electricity bills, lower carbon taxes, and deferred capital on grid upgrades, while integration with OSS/BSS ensures changes are logged and reversible.

Implementation requires deploying lightweight agents at the RAN Intelligent Controller (RIC) or Service Management and Orchestration (SMO) layer, integrated via O-RAN or vendor APIs into baseband units and site controllers. The central orchestrator uses reinforcement learning to refine policies, but all actions pass through a governance layer that enforces safety rules—like minimum coverage cells—and requires human approval for major parameter changes. Observability is built in, with dashboards tracking energy vs. performance trade-offs and audit trails for ESG reporting. Rollout is phased, starting with low-risk suburban sites before moving to dense urban grids.

This workflow directly targets the 20-40% of RAN OPEX attributed to energy. It automates the manual, reactive process of adjusting site power states by deploying a coordinating fleet of specialized agents. These agents ingest live traffic, weather, grid carbon intensity, and hardware telemetry to execute predictive, policy-compliant power adjustments. The system integrates with RAN Intelligent Controllers (RICs), EMS, and site controllers to enact changes, creating a continuous optimization loop that reduces costs and supports ESG reporting without compromising service-level agreements.

Implementation requires deploying containerized agents within a secure orchestration layer like Kubernetes, integrated via APIs with OSS/BSS and the RIC. Critical controls include simulation-based pre-rollout validation in a digital twin, human-in-the-loop approval gates for major policy changes, and continuous monitoring for KPI drift. The architecture must handle exceptions—like sudden traffic surges—by routing to a human NOC with recommended compensating actions, ensuring operational safety while maximizing automated savings.

This workflow directly targets the 20-40% of RAN OPEX attributed to energy. It automates the manual, reactive process of adjusting site power states by deploying specialized agents for prediction, coordination, and control. A forecasting agent analyzes traffic and topology to schedule safe sleep modes, while a real-time orchestrator fine-tunes amplifier output and cooling based on live telemetry. This creates a continuous, closed-loop optimization layer integrated with the RAN Intelligent Controller (RIC) and element management systems, turning energy from a fixed cost into a dynamically managed variable.

Implementation follows a phased, risk-managed rollout. Phase 1 deploys the predictive agent in shadow mode, validating forecasts against actual load. Phase 2 activates orchestration for non-critical, low-traffic sites with strict guardrails and a mandatory human-in-the-loop approval gate for all actions. Phase 3 scales to the full network, with the orchestrator gaining autonomy for pre-approved actions while routing exceptions to the NOC. The architecture is built on LangGraph for agent coordination, integrates via O-RAN RIC xApps, and includes comprehensive observability dashboards for carbon and cost savings tracking.

This workflow automates the continuous optimization of energy consumption across a distributed RAN by coordinating specialized agents for sleep modes, amplifier tuning, and cooling systems. It directly targets OPEX, reducing site power bills by 15-25% while maintaining strict performance SLAs. The architecture integrates with existing EMS, BSS, and power controllers, using real-time traffic and grid carbon data to make orchestrated decisions that balance cost, sustainability, and KPI preservation without manual intervention.

Implementation requires a phased rollout, starting with non-critical cells, using a digital twin for simulation. Each agent's actions are gated through a policy engine that enforces thermal, coverage, and SLA guardrails before execution. The observability layer provides real-time dashboards and automatic rollback triggers if KPIs degrade, ensuring reliability. This creates a closed-loop system where savings are auditable, changes are traceable, and the network self-optimizes within defined operational boundaries.

This workflow automates the continuous balancing of sleep modes, amplifier settings, and cooling systems across thousands of sites. It directly targets the OPEX of energy, which can constitute over 20% of network operating costs. The architecture ingests live telemetry from RAN controllers (e.g., Ericsson, Nokia), OSS platforms, and power systems, using specialized agents to model trade-offs between energy use and performance SLAs. Savings come from eliminating conservative, static power profiles and enabling precise, conditional power-downs that manual operations cannot safely execute at scale.

Implementation requires agents integrated via the RAN Intelligent Controller (RIC) for near-real-time control and Element Management Systems (EMS) for direct hardware commands. Critical controls include a policy engine to veto unsafe actions, A/B testing frameworks to validate savings, and rollback triggers for KPI degradation. Observability must track energy savings per site against a baseline while monitoring for SLA drift, creating a closed-loop system that justifies the integration effort through measurable OPEX reduction and improved ESG reporting.