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Course Outline
Introduction to Agentic AI in Operations
- The evolution of IT automation: shifting from static runbooks to reasoning agents
- Understanding agent anatomy: the reasoning loop, tool utilization, memory mechanisms, and planning strategies
- Determining when to automate tasks and when to retain human oversight
Agent Frameworks and Architectural Design
- Single-agent patterns: exploring ReAct, Plan-and-Execute, and tool-calling loops
- Multi-agent architectures: supervisor, hierarchical, and swarm-based patterns
- Comparative analysis of frameworks: LangGraph, CrewAI, AutoGen, and custom agent solutions
- Developing your first operational agent: querying monitoring systems, diagnosing issues, and proposing solutions
Tool Integration for IT Operations
- Connecting agents to APIs from Prometheus, Grafana, Datadog, and PagerDuty
- Agent-driven log querying: integration with Elasticsearch, Loki, and Splunk
- Leveraging infrastructure tools: executing kubectl, Terraform, and Ansible via agent actions
- Designing secure tool interfaces with parameter validation and idempotency
Automating Incident Response
- Automated incident triage: classifying severity and routing tickets
- Generating root cause hypotheses and gathering supporting evidence
- Executing automated remediations: restarting, scaling, rolling back, and failover actions
- Creating an incident runbook agent with progressive autonomy levels
Safety, Guardrails, and Human-in-the-Loop Mechanisms
- Classifying actions: read-only, low-risk, high-risk, and destructive operations
- Establishing approval gates and escalation policies for critical operations
- Implementing guardrail patterns: action allowlists, blast radius limitations, and rollback guarantees
- Maintaining audit trails and decision provenance for compliance purposes
Multi-Agent Orchestration for Complex Incidents
- Coordinating specialist agents: triage, diagnosis, and remediation roles
- Managing inter-agent communication and shared context
- Resolving conflicts when agents propose contradictory actions
- Simulating end-to-end major incidents with multi-agent response strategies
Observability and Evaluation
- Tracing agent reasoning chains for debugging and audit purposes
- Evaluating agent decision quality: measuring precision, recall, and time-to-resolution
- Establishing feedback loops: learning from operator overrides and final outcomes
- Tracking costs and analyzing token economics for operational agents
Production Deployment and Operations
- Deploying agents as services: utilizing APIs, webhooks, and scheduled jobs
- Rolling out gradual autonomy: transitioning from shadow mode to full auto-remediation
- Creating runbooks for agent failures: procedures for when the agent itself breaks
- Building the business case and measuring ROI for autonomous operations
Requirements
- Professional experience with IT operations, DevOps, or SRE practices.
- Familiarity with Python scripting and REST APIs.
- Basic understanding of LLM capabilities and prompt engineering.
Target Audience
- SRE and DevOps engineers exploring AI-driven automation.
- Platform engineers focused on building self-healing infrastructure.
- IT operations leads evaluating agentic AI for incident management.
14 Hours
