HN837: Agentic AI to Reduce MTTR

Agentic AI transforms network engineering by shifting roles from reactive firefighting to proactive system architecture. By implementing deterministic workflows and a single source of truth—typically a structured YAML file—engineers can automate incident resolution. This architecture utilizes the Model Context Protocol to gather real-time telemetry, which is then analyzed by fine-tuned, vendor-agnostic models. To ensure reliability, proposed solutions are tested against digital twins built in ContainerLab, creating a closed-loop system that validates changes before implementation. Eduard Dulharu emphasizes that success relies on high-quality, curated datasets and rigorous empirical testing rather than relying on generic frontier models. This approach reduces MTTR from hours to seconds by providing engineers with evidence-based recommendations, effectively turning AI into a force multiplier that remains under human control and oversight.