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Course Outline
Introduction to Agentic AI in Operations
- The evolution from static runbooks to intelligent reasoning agents in IT automation
- Anatomy of an agent: reasoning loops, tool utilization, memory management, and planning
- Determining when to automate versus when to retain human intervention
Agent Frameworks and System Architectures
- Single-agent patterns: ReAct, Plan-and-Execute, and iterative tool-calling loops
- Multi-agent structures: supervisor, hierarchical, and swarm coordination patterns
- Comparative analysis of frameworks: LangGraph, CrewAI, AutoGen, and custom agent solutions
- Constructing your first operational agent: querying monitors, diagnosing issues, and proposing solutions
Integrating Tools for IT Operations
- Connecting agents to APIs such as Prometheus, Grafana, Datadog, and PagerDuty
- Agent-driven log querying using Elasticsearch, Loki, and Splunk integrations
- Utilizing infrastructure tools like kubectl, Terraform, and Ansible through agent actions
- Designing secure tool interfaces with parameter validation and idempotent operations
Automating Incident Response
- Automated triage processes: severity classification and intelligent routing
- Generating root cause hypotheses and gathering supporting evidence
- Executing automated remediation tasks: restarts, scaling, rollbacks, and failovers
- Developing an incident runbook agent with adjustable autonomy levels
Safety Protocols, Guardrails, and Human Oversight
- Classifying actions by risk level: read-only, low-risk, high-risk, and destructive
- Implementing approval gates and escalation policies for critical operations
- Applying guardrail strategies: action allowlists, blast radius constraints, and rollback assurances
- Maintaining audit trails and decision provenance for regulatory compliance
Orchestrating Multi-Agent Systems for Complex Incidents
- Coordinating specialized agents: triage, diagnosis, and remediation specialists
- Managing inter-agent communication and shared context
- Resolving conflicts when agents suggest contradictory actions
- Simulating end-to-end major incidents with multi-agent coordination
Observability and Performance Evaluation
- Tracing agent reasoning chains to facilitate debugging and auditing
- Assessing decision quality through metrics like precision, recall, and resolution time
- Implementing feedback loops to learn from operator overrides and outcomes
- Tracking costs and analyzing token economics for operational agents
Production Deployment and Ongoing Operations
- Deploying agents as services using APIs, webhooks, and scheduled jobs
- Implementing gradual autonomy rollouts from shadow mode to full auto-remediation
- Preparing runbooks for agent failures: handling scenarios where the agent itself malfunctions
- Building the business case and measuring ROI for autonomous operations
Requirements
- Practical experience in IT operations, DevOps, or SRE methodologies.
- Proficiency in Python scripting and working with REST APIs.
- A foundational understanding of Large Language Model (LLM) capabilities and prompt engineering.
Target Audience
- SRE and DevOps engineers looking to leverage AI-driven automation.
- Platform engineers focused on building self-healing infrastructure.
- IT operations leaders evaluating agentic AI for improved incident management.
14 Hours
