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Course Outline
Introduction to Agentic AI in Operations
- Evolution of IT automation: moving from static runbooks to reasoning agents
- Agent anatomy: understanding the reasoning loop, tool utilisation, memory, and planning
- Determining when to automate versus when to retain human oversight
Agent Frameworks and Architectures
- Single-agent patterns: ReAct, Plan-and-Execute, and tool-calling loops
- Multi-agent structures: supervisor, hierarchical, and swarm models
- Framework comparison: LangGraph, CrewAI, AutoGen, and custom-built agents
- Constructing your first operational agent: querying, diagnosing, and proposing solutions
Tool Integration for IT Operations
- Connecting agents to Prometheus, Grafana, Datadog, and PagerDuty APIs
- Agent-driven log queries: integration with Elasticsearch, Loki, and Splunk
- Infrastructure automation: using kubectl, Terraform, and Ansible via agent actions
- Designing secure tool interfaces with parameter validation and idempotency
Incident Response Automation
- Automated triage: severity classification and intelligent routing
- 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
- Classifying actions: read-only, low-risk, high-risk, and destructive
- Establishing approval gates and escalation policies for critical operations
- Implementing guardrails: action allowlists, blast radius limits, and rollback guarantees
- Maintaining audit trails and decision provenance for compliance
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 incident responses using multiple agents
Observability and Evaluation
- Tracing agent reasoning chains to facilitate debugging and auditing
- Evaluating decision quality: precision, recall, and time-to-resolution
- Establishing feedback loops: learning from operator overrides and final outcomes
- Tracking costs and analysing token economics for operational agents
Production Deployment and Operations
- Deploying agents as services: APIs, webhooks, and scheduled jobs
- Gradual autonomy rollout: transitioning from shadow mode to full auto-remediation
- Handling agent failures: runbooks for when the agent itself malfunctions
- Building the business case and measuring ROI for autonomous operations
Requirements
- Practical experience with IT operations, DevOps, or SRE methodologies.
- 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 focused on building self-healing infrastructure.
- IT operations leaders assessing Agentic AI for incident management.
14 Hours
