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Course Outline
Introduction to Agentic AI for Operations
- Evolution of IT automation: moving from static runbooks to reasoning agents
- Anatomy of an agent: understanding the reasoning loop, tool usage, memory, and planning
- Decision-making framework: determining when to automate and when to involve humans
Agent Frameworks and Architectures
- Single-agent patterns: exploring ReAct, Plan-and-Execute, and tool-calling loops
- Multi-agent architectures: supervisor, hierarchical, and swarm models
- Framework comparison: LangGraph, CrewAI, AutoGen, and custom agent implementations
- Building your first operational agent: querying monitoring, diagnosing issues, and proposing solutions
Tool Integration for IT Operations
- Connecting agents to Prometheus, Grafana, Datadog, and PagerDuty APIs
- Agent-driven log querying: integrating Elasticsearch, Loki, and Splunk
- Infrastructure tool usage: executing kubectl, Terraform, and Ansible commands via agent actions
- Designing secure tool interfaces with parameter validation and idempotency
Incident Response Automation
- Automated incident triage: classifying severity and routing alerts
- Generating root cause hypotheses and gathering supporting evidence
- Automated remediation: executing restart, scale, rollback, and failover actions
- Creating an incident runbook agent with progressive levels of autonomy
Safety, Guardrails, and Human-in-the-Loop
- Action classification: distinguishing between read-only, low-risk, high-risk, and destructive operations
- Establishing approval gates and escalation policies for critical actions
- Guardrail patterns: implementing action allowlists, blast radius limits, 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 responses
Observability and Evaluation
- Tracing agent reasoning chains for debugging and auditing
- Evaluating agent decision quality: measuring precision, recall, and time-to-resolution
- Implementing feedback loops to learn from operator overrides and outcomes
- Tracking costs and managing 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
- Handling agent failures: operational runbooks for when the AI system breaks
- Building the business case and measuring ROI for autonomous operations
Requirements
- Practical experience with IT operations, DevOps, or SRE practices.
- Proficiency in Python scripting and REST API interactions.
- A foundational understanding of Large Language Model (LLM) capabilities and prompt engineering.
Target Audience
- SRE and DevOps engineers exploring AI-driven automation strategies.
- Platform engineers focused on building self-healing infrastructure.
- IT operations leaders evaluating agentic AI for enhanced incident management.
14 Hours
