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Course Outline
Foundations of Agentic AI in Operations
- Evolution from static runbooks to reasoning agents: the progression of IT automation.
- Anatomy of an agent: reasoning loops, tool utilization, memory, and planning.
- Determining when to automate versus when to maintain human oversight.
Agent Frameworks and Architectural Patterns
- Single-agent patterns: ReAct, Plan-and-Execute, and tool-calling loops.
- Multi-agent architectures: supervisor, hierarchical, and swarm models.
- Framework comparison: LangGraph, CrewAI, AutoGen, and custom agent solutions.
- Building your first 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 with Elasticsearch, Loki, and Splunk integrations.
- Utilizing infrastructure tools: kubectl, Terraform, and Ansible via agent actions.
- Designing secure tool interfaces with parameter validation and idempotency.
Automated Incident Response
- Automated incident triage: severity classification and intelligent routing.
- Generating root cause hypotheses and gathering supporting evidence.
- Automated remediation strategies: restart, scaling, rollback, and failover actions.
- Constructing an incident runbook agent with progressive levels of autonomy.
Safety, Guardrails, and Human-in-the-Loop Protocols
- Action classification: read-only, low-risk, high-risk, and destructive operations.
- Implementing approval gates and escalation policies for critical operations.
- Guardrail patterns: action allowlists, blast radius limitations, 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 units.
- Managing inter-agent communication and shared context.
- Resolving conflicts when agents propose contradictory actions.
- Conducting end-to-end major incident simulations with multi-agent responses.
Observability and Evaluation
- Tracing agent reasoning chains for debugging and audit purposes.
- Evaluating agent decision quality: precision, recall, and time-to-resolution.
- Establishing feedback loops: learning from operator overrides and outcomes.
- Tracking costs and managing 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.
- Runbook for agent failures: protocols for when the agent itself 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 APIs.
- Fundamental understanding of 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 incident management.
14 Hours
