Self-Healing Network Operations: Autonomous AI Agent Workflows for Detection, Diagnosis, and Automated Remediation

Alert fatigue, brittle runbooks, and 3 AM escalations are pushing network engineering teams to their breaking point.
As distributed infrastructure scales, static scripts and rigid monitoring rules can no longer keep up with complex failure modes. Moving beyond simple alerting requires autonomous systems that can ingest telemetry, reason through multi-hop anomalies, and execute safe remediations in real time.
Self-Healing Network Operations is an advanced, practical guide engineered specifically for senior network engineers, SREs, and DevOps architects. It bypasses theoretical hype to deliver production-ready architectures for building, deploying, and governing autonomous AI agent workflows across modern networks.
Through realistic lab environments using Containerlab, GNS3, and live telemetry pipelines, you will learn how to build agentic feedback loops, implement strict execution guardrails, and leverage the Model Context Protocol (MCP) to safely interface LLMs with routing hardware and cloud fabrics.
Inside the Book, You Will Build and Deploy:
Live Telemetry & Monitoring Agents: Ingest SNMP, syslog, and NetFlow streams into LLM-driven agents to detect silent degradations and summarize network health.
Root-Cause Reasoning Loops: Build diagnostic agents that safely execute CLI/API diagnostic commands to isolate multi-hop routing failures and resolve ambiguous symptoms.
Standardized MCP Tooling: Package network diagnostics as robust Model Context Protocol (MCP) servers with versioning and security boundaries.
Closed-Loop Automated Remediation: Design confidence-scored execution pipelines featuring dry-run gates, automated rollbacks, config-drift auto-healing, and blast-radius circuit breakers.
Multi-Agent NetOps Systems: Implement hierarchical supervisor-worker agent topologies to coordinate parallel monitoring, diagnosis, and remediation tasks.
Agent Security & Compliance: Harden autonomous workflows against prompt injection via device logs, manage least-privilege credentials, and log all autonomous actions for enterprise audit readiness.
Eliminate manual incident triage and bridge the gap between deterministic network protocols and autonomous AI. Whether you manage enterprise enterprise backbones, multi-cloud transit networks, or data center fabrics, this book provides the blueprint for building dependable, self-healing network operations.
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