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Course Outline
Introduction to Agentic AI for Operations
- Transitioning from static runbooks to reasoning agents: the evolution of IT automation
- Agent architecture: reasoning loops, tool utilization, memory, and planning
- Determining when to automate versus when to maintain human involvement
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 agents
- Constructing your first operational agent: querying monitoring, diagnosing issues, and proposing solutions
Tool Integration for IT Operations
- Linking agents to Prometheus, Grafana, Datadog, and PagerDuty APIs
- Agent-based log querying: integration with Elasticsearch, Loki, and Splunk
- Utilizing infrastructure tools via agent actions: kubectl, Terraform, and Ansible
- Designing secure tool interfaces through parameter validation and idempotency
Incident Response Automation
- Automated incident triage: severity classification and routing
- Generating root cause hypotheses and collecting evidence
- Automated remediation: executing restarts, scaling, rollbacks, and failovers
- Creating an incident runbook agent with progressive autonomy levels
Safety, Guardrails, and Human-in-the-Loop
- Classifying actions: read-only, low-risk, high-risk, and destructive
- Establishing approval gates and escalation policies for critical operations
- Guardrail strategies: action allowlists, blast radius limits, and rollback guarantees
- Maintaining audit trails and decision provenance for compliance
Multi-Agent Orchestration for Complex Incidents
- Coordinating specialized agents: triage, diagnosis, and remediation roles
- Managing inter-agent communication and shared context
- Resolving conflicts when agents propose contradictory actions
- Simulating major incidents end-to-end with multi-agent response
Observability and Evaluation
- Tracing agent reasoning chains for debugging and auditing
- Assessing agent decision quality: precision, recall, and time-to-resolution
- Implementing feedback loops: learning from operator overrides and outcomes
- Tracking costs and managing token economics for operational agents
Production Deployment and Operations
- Deploying agents as services: APIs, webhooks, and scheduled jobs
- Implementing gradual autonomy rollout: from shadow mode to full auto-remediation
- Establishing runbooks for agent failures: protocols for when the agent itself encounters issues
- Building the business case and measuring ROI for autonomous operations
Requirements
- Practical experience in IT operations, DevOps, or SRE practices.
- Proficiency in Python scripting and REST APIs.
- Fundamental understanding 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 evaluating agentic AI for incident management.
14 Hours
