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Course Outline
Introduction to Agentic AI for Operations
- Evolution of IT automation: moving from static runbooks to reasoning agents
- Agent anatomy: understanding reasoning loops, tool utilization, memory, and planning
- Determining when to automate processes versus when to maintain human oversight
Agent Frameworks and Architectures
- Single-agent patterns: ReAct, Plan-and-Execute, and tool-calling loops
- Multi-agent architectures: supervisor, hierarchical, and swarm models
- Comparing frameworks: LangGraph, CrewAI, AutoGen, and custom agent solutions
- Developing 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
- Log querying via agents: integrating with Elasticsearch, Loki, and Splunk
- Utilizing infrastructure tools through agent actions: kubectl, Terraform, and Ansible
- Designing secure tool interfaces with parameter validation and idempotency
Incident Response Automation
- Automating incident triage: severity classification and 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
- Action classification: distinguishing read-only, low-risk, high-risk, and destructive operations
- Establishing approval gates and escalation policies for critical tasks
- Implementing guardrail patterns: 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 suggest contradictory actions
- Simulating end-to-end major incident responses with multi-agent coordination
Observability and Evaluation
- Tracing agent reasoning chains to facilitate debugging and auditing
- Assessing agent decision quality: precision, recall, and time-to-resolution
- Creating feedback loops: learning from operator overrides and operational outcomes
- Tracking costs and managing token economics for operational agents
Production Deployment and Operations
- Deploying agents as services: APIs, webhooks, and scheduled jobs
- Rolling out gradual autonomy: progressing from shadow mode to full auto-remediation
- Managing agent failures: protocols for when the agent itself encounters issues
- Building the business case and measuring ROI for autonomous operations
Requirements
- Practical experience with IT operations, DevOps, or SRE practices.
- Proficiency with Python scripting and REST APIs.
- A foundational understanding of LLM capabilities and prompt engineering.
Audience
- SRE and DevOps engineers investigating AI-driven automation.
- Platform engineers focused on building self-healing infrastructure.
- IT operations leaders assessing agentic AI for incident management.
14 Hours
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 3200 € + VAT*
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