Autonomous Operations with AI Agents Training Course

Introduction to Agentic AI in Operations

• Transitioning from static runbooks to reasoning agents: the evolution of IT automation
• Agent structure: reasoning loops, tool utilization, memory, and planning
• Determining when to automate and when to retain human oversight

Agent Frameworks and Architectures

• Single-agent patterns: ReAct, Plan-and-Execute, and tool-calling loops
• Multi-agent architectures: supervisor, hierarchical, and swarm models
• Framework comparison: LangGraph, CrewAI, AutoGen, and custom agent solutions
• Developing your first operational agent: querying monitoring, diagnosing, and proposing solutions

Tool Integration for IT Operations

• Connecting agents to Prometheus, Grafana, Datadog, and PagerDuty APIs
• Log querying with agents: Elasticsearch, Loki, and Splunk integration
• Infrastructure tool usage: kubectl, Terraform, Ansible via agent actions
• Designing secure tool interfaces with parameter validation and idempotency

Incident Response Automation

• Automated incident triage: severity classification and routing
• Generating root cause hypotheses and gathering evidence
• Automated remediation: restart, scale, rollback, and failover actions
• Creating an incident runbook agent with progressive levels of autonomy

Safety, Guardrails, and Human-in-the-Loop

• Action classification: read-only, low-risk, high-risk, and destructive operations
• Approval gates and escalation policies for critical operations
• Guardrail patterns: action allowlists, blast radius limits, and rollback guarantees
• Audit trails and decision provenance for compliance

Multi-Agent Orchestration for Complex Incidents

• Coordinating specialist agents: triage, diagnosis, and remediation agents
• Inter-agent communication and shared context management
• Conflict resolution when agents propose contradictory actions
• End-to-end major incident simulation using multi-agent response

Observability and Evaluation

• Tracing agent reasoning chains for debugging and auditing
• Evaluating agent decision quality: precision, recall, and time-to-resolution
• Feedback loops: learning from operator overrides and outcomes
• Cost tracking and token economics for operational agents

Production Deployment and Operations

• Deploying agents as services: APIs, webhooks, and scheduled jobs
• Gradual autonomy rollout: from shadow mode to full auto-remediation
• Runbook for agent failures: handling situations where the agent itself malfunctions
• Building the business case and measuring ROI for autonomous operations