Inmanta CEO advocates model-driven orchestration to bridge network autonomy chasm

The shift toward model-driven orchestration marks a departure from reactive troubleshooting to predictive, adaptive control loops. For streaming infrastructure, this transition is critical as services increasingly span multiple administrative domains and hybrid cloud environments where manual intervention causes costly downtime. As networks become more disaggregated, the ability to maintain convergence between intent and reality will separate efficient operators from those burdened by escalating operational complexity. This move forces a cultural merger between NetOps and DevOps, treating network functions as versioned software abstractions rather than static hardware. Watch for increased adoption of TM Forum ODA standards as a benchmark for vendor interoperability in autonomous deployments.

The industry is increasingly prioritizing AI-native network architectures to handle the scale of modern streaming traffic, with agentic AI emerging as a key focus for future media workflows. To further improve efficiency, operators are also looking at telecom network automation requirements to streamline complex backend tasks, while telcos accelerate AI network modernization to boost margins. As these systems evolve, broadcast orchestration platforms are becoming essential for managing the complexity of modern broadcast and digital workflows, especially as broadcasters lose 75% of production time to manual technical tasks. For those looking to scale infrastructure rapidly, Aranya AI cluster deployment offers a glimpse into how automated provisioning can reduce time-to-market, while live stream orchestration provides a proven path for saving operational hours. The embedded systems market growth further underscores the hardware-software convergence required for these next-generation networks, with Cisco Unified Edge AI now targeting the surge in agentic traffic.