The flight deck layer for autonomous AI networks

As AI networks act autonomously, embedded observability is evolving into a governing layer that orchestrates telemetry, policy and real-time corrective action.

- BY SIMON RIZKALLA

For technology leaders who have spent decades in the trenches of distributed systems, observability was akin to forensic archaeology. Engineering teams spent nights digging through disparate logs and fragmented traces, trying to reconstruct a crime scene after a critical service had failed.

We are now in the era of autonomous, Al-driven networks, where these reactive approaches pose an existential risk. C-Level technology leaders recognise that the world is moving away from a deterministic, “if-then” logic to a probabilistic reality, one shaped by agentic Al and self-healing infrastructures.

In this new age, observability must transcend its role as a diagnostic window to a true control plane, the nervous system dictating how intelligent networks operate, scale, and protect themselves.

FROM REACTIVE MONITORING TO ACTIVE CONTROL

The black-box nature of an Al-driven network's decision-making poses a major challenge for organisations. Traditional monitoring tools, built for static environments, are blind to the nuances of prompt engineering, vector database latency, and LLM and Al agent hallucinations. When an Al agent reroutes network traffic or allocates New compute resources based on a shifting model, it is not following a scripted model; it is responding to data patterns.