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Course Outline
Foundations of Agentic AI in Operations
- Transitioning from static runbooks to reasoning agents: The evolution of IT automation
- Anatomy of an agent: Reasoning loops, tool utilization, memory, and planning
- Determining when to automate versus when to retain human oversight
Agent Frameworks and System Architectures
- Single-agent patterns: ReAct, Plan-and-Execute, and tool-calling cycles
- Multi-agent architectures: Supervisor, hierarchical, and swarm models
- Framework evaluation: LangGraph, CrewAI, AutoGen, and custom agent implementations
- Developing a basic operational agent: Querying monitoring, diagnosing, and proposing solutions
Tool Integration for IT Operations
- Linking agents to Prometheus, Grafana, Datadog, and PagerDuty APIs
- 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 with parameter validation and idempotency
Automating Incident Response
- Automated triage: Severity classification and ticket routing
- Generating root cause hypotheses and collecting supporting evidence
- Executing remediation: Restarting, scaling, rolling back, and failover actions
- Creating an incident runbook agent with staged autonomy levels
Safety Protocols, Guardrails, and Human Oversight
- Action categorization: Read-only, low-risk, high-risk, and destructive operations
- Establishing approval gates and escalation policies for critical tasks
- Guardrail strategies: Action allowlists, blast radius constraints, and rollback assurances
- Maintaining audit trails and decision provenance for compliance purposes
Multi-Agent Orchestration for Complex Scenarios
- Coordinating specialized agents: Triage, diagnosis, and remediation roles
- Managing inter-agent communication and shared context
- Resolving conflicts when agents suggest opposing actions
- Simulating major incidents with a multi-agent response strategy
Observability and Performance Evaluation
- Tracing agent reasoning chains for debugging and auditing
- Assessing decision quality: Measuring precision, recall, and resolution time
- Feedback mechanisms: Learning from operator overrides and operational outcomes
- Tracking costs and token economics for operational agents
Production Deployment and Ongoing Operations
- Deploying agents as services: Utilizing APIs, webhooks, and scheduled tasks
- Phased autonomy rollout: Moving from shadow mode to full auto-remediation
- Managing agent failures: Procedures for when the agent itself encounters issues
- Building the business case: 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 LLM capabilities and prompt engineering techniques.
Target Audience
- SRE and DevOps engineers exploring AI-driven automation strategies.
- Platform engineers developing self-healing infrastructure solutions.
- IT operations leaders assessing the potential of agentic AI for incident management.
14 Hours
