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Cursusaanbod
Introduction to Agentic AI for Operations
- From static runbooks to reasoning agents: the evolution of IT automation
- Agent anatomy: reasoning loop, tool use, memory, and planning
- When to automate vs when to keep humans in the loop
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 agents
- Building your first operational agent: query monitoring, diagnose, propose
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 use: kubectl, Terraform, Ansible via agent actions
- Designing safe tool interfaces with parameter validation and idempotency
Incident Response Automation
- Automated incident triage: severity classification and routing
- Root cause hypothesis generation and evidence gathering
- Automated remediation: restart, scale, rollback, and failover actions
- Building an incident runbook agent 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 limits, and rollback guarantees
- Audit trails and decision provenance for compliance
Multi-Agent Orchestration for Complex Incidents
- Coordinating specialist agents: triage agent, diagnosis agent, remediation agent
- Inter-agent communication and shared context management
- Conflict resolution when agents propose contradictory actions
- End-to-end major incident simulation with multi-agent response
Observability and Evaluation
- Tracing agent reasoning chains for debugging and audit
- Evaluating agent decision quality: precision, recall, 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: shadow mode to full auto-remediation
- Runbook for agent failures: what happens when the agent itself breaks
- Building the business case and measuring ROI for autonomous operations
Vereisten
- Experience with IT operations, DevOps, or SRE practices.
- Familiarity with Python scripting and REST APIs.
- Basic understanding of LLM capabilities and prompt engineering.
Audience
- SRE and DevOps engineers exploring AI-driven automation.
- Platform engineers building self-healing infrastructure.
- IT operations leads evaluating agentic AI for incident management.
14 Uren
