Autonomous Operations with AI Agents Training Course

Introduction to Agentic AI for Operations

• Evolution of IT automation: From static runbooks to reasoning agents
• Agent anatomy: Exploring the reasoning loop, tool utilization, memory, and planning
• Determining when to automate versus 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 patterns
• Framework comparison: LangGraph, CrewAI, AutoGen, and custom agent implementations
• Building your first operational agent: Querying monitoring, diagnosing issues, and proposing solutions

Tool Integration for IT Operations

• Connecting agents to APIs of Prometheus, Grafana, Datadog, and PagerDuty
• Log querying with agents: Integration with Elasticsearch, Loki, and Splunk
• Infrastructure tool usage: Executing kubectl, Terraform, and Ansible commands 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 supporting evidence
• Automated remediation: Executing restart, scaling, rollback, and failover actions
• Creating an incident runbook agent with progressive autonomy levels

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

• Action classification: Differentiating between read-only, low-risk, high-risk, and destructive operations
• Establishing approval gates and escalation policies for critical operations
• Guardrail patterns: Implementing action allowlists, blast radius limits, and rollback guarantees
• Maintaining audit trails and decision provenance for compliance

Multi-Agent Orchestration for Complex Incidents

• Coordinating specialist agents: Triage, diagnosis, and remediation agents
• Managing inter-agent communication and shared context
• Resolving conflicts when agents propose contradictory actions
• Simulating end-to-end major incidents with multi-agent responses

Observability and Evaluation

• Tracing agent reasoning chains for debugging and audit purposes
• Evaluating agent decision quality: Assessing precision, recall, and time-to-resolution
• Establishing feedback loops: Learning from operator overrides and outcomes
• Tracking costs and analyzing token economics for operational agents

Production Deployment and Operations

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