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Course Outline
Foundations of Agentic AI in Operations
- The shift from static runbooks to reasoning agents: the progression of IT automation
- Component analysis: reasoning loops, tool utilization, memory, and planning
- Determining when to automate versus when to retain human involvement
Agent Frameworks and Structural Designs
- Single-agent methodologies: ReAct, Plan-and-Execute, and tool-calling cycles
- Multi-agent structures: supervisor, hierarchical, and swarm configurations
- Framework evaluation: LangGraph, CrewAI, AutoGen, and custom agent development
- Creating your initial operational agent: querying monitoring, diagnosing issues, and proposing solutions
Tool Integration for IT Operations
- Connecting agents to APIs for 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 featuring parameter validation and idempotency
Automating Incident Response
- Automated incident triage: classifying severity and routing responses
- Generating root cause hypotheses and collecting supporting evidence
- Automated corrective actions: restarting, scaling, rolling back, and failing over
- Constructing an incident runbook agent with progressive levels of autonomy
Safety, Guardrails, and Human Oversight
- Classifying actions: read-only, low-risk, high-risk, and destructive
- Establishing approval gates and escalation protocols for critical operations
- Guardrail strategies: action allowlists, blast radius constraints, and rollback assurances
- Maintaining audit trails and decision provenance for compliance adherence
Orchestrating Multi-Agent Systems for Complex Incidents
- Coordinating specialized agents: triage, diagnosis, and remediation units
- Managing inter-agent communication and shared context
- Resolving conflicts when agents suggest opposing actions
- Simulating end-to-end major incidents with multi-agent responses
Observability and Performance Evaluation
- Tracing agent reasoning chains for debugging and audit purposes
- Assessing agent decision quality: precision, recall, and resolution time
- Implementing feedback loops: learning from operator overrides and outcomes
- Tracking costs and analyzing token economics for operational agents
Production Deployment and Operational Management
- Deploying agents as services: utilizing APIs, webhooks, and scheduled tasks
- Phased autonomy rollout: transitioning from shadow mode to full auto-remediation
- Runbooks for agent failures: procedures when the agent itself malfunctions
- Developing business cases and measuring ROI for autonomous operations
Requirements
- Practical experience in IT operations, DevOps, or SRE methodologies.
- Proficiency in Python scripting and REST API interactions.
- Fundamental knowledge of Large Language Model (LLM) capabilities and prompt engineering.
Target Audience
- SRE and DevOps engineers interested in AI-driven automation.
- Platform engineers focused on creating self-healing infrastructure.
- IT operations leaders evaluating agentic AI solutions for incident management.
14 Hours
