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Course Outline
Introduction to Agentic AI for Operations
- The evolution of IT automation: from static runbooks to reasoning agents
- Agent structure: reasoning loops, tool utilization, memory, and planning
- Determining when to automate versus retaining 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
- Creating your first operational agent: monitoring queries, diagnostics, and proposals
Tool Integration for IT Operations
- Connecting agents to Prometheus, Grafana, Datadog, and PagerDuty APIs
- Agent-driven log querying: integration with Elasticsearch, Loki, and Splunk
- Leveraging infrastructure tools: kubectl, Terraform, and 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 actions: restarts, scaling, rollbacks, and failovers
- Developing incident runbook agents with progressive autonomy levels
Safety, Guardrails, and Human-in-the-Loop
- Action classification: read-only, low-risk, high-risk, and destructive
- Approval gates and escalation policies for critical operations
- Guardrail patterns: action allowlists, blast radius limitations, and rollback assurances
- Audit trails and decision provenance for compliance purposes
Multi-Agent Orchestration for Complex Incidents
- Coordinating specialized agents: triage, diagnosis, and remediation agents
- Inter-agent communication and shared context management
- Resolving conflicts when agents suggest contradictory actions
- End-to-end major incident simulation using multi-agent response
Observability and Evaluation
- Tracing agent reasoning chains for debugging and auditing
- Evaluating decision quality: precision, recall, and time-to-resolution
- Feedback loops: learning from operator overrides and final outcomes
- Cost tracking and token economics for operational agents
Production Deployment and Operations
- Deploying agents as services: APIs, webhooks, and scheduled tasks
- Phased autonomy rollout: transitioning from shadow mode to full auto-remediation
- Agent failure management: protocols for when the agent itself encounters issues
- Building the business case and measuring ROI for autonomous operations
Requirements
- Background in IT operations, DevOps, or SRE practices.
- Proficiency in Python scripting and REST APIs.
- Foundational knowledge of LLM capabilities and prompt engineering.
Target Audience
- SRE and DevOps engineers exploring AI-driven automation.
- Platform engineers developing self-healing infrastructure.
- IT operations leaders assessing agentic AI for incident management.
14 Hours
