Mulțumim pentru trimiterea solicitării! Un membru al echipei noastre vă va contacta în curând.
Mulțumim pentru trimiterea rezervării! Un membru al echipei noastre vă va contacta în curând.
Schița de curs
Introduction to Agentic AI for Operations
- From static runbooks to reasoning agents: the evolution of IT automation
- Agent anatomy: reasoning loop, tool use, memory, and planning
- When to automate vs when to keep humans in the loop
Agent Frameworks and Architectures
- Single-agent patterns: ReAct, Plan-and-Execute, and tool-calling loops
- Multi-agent architectures: supervisor, hierarchical, and swarm patterns
- Framework comparison: LangGraph, CrewAI, AutoGen, and custom agents
- Building your first operational agent: query monitoring, diagnose, propose
Tool Integration for IT Operations
- Connecting agents to Prometheus, Grafana, Datadog, and PagerDuty APIs
- Log querying with agents: Elasticsearch, Loki, and Splunk integration
- Infrastructure tool use: kubectl, Terraform, Ansible via agent actions
- Designing safe tool interfaces with parameter validation and idempotency
Incident Response Automation
- Automated incident triage: severity classification and routing
- Root cause hypothesis generation and evidence gathering
- Automated remediation: restart, scale, rollback, and failover actions
- Building an incident runbook agent with progressive autonomy levels
Safety, Guardrails, and Human-in-the-Loop
- Action classification: read-only, low-risk, high-risk, and destructive
- Approval gates and escalation policies for critical operations
- Guardrail patterns: action allowlists, blast radius limits, and rollback guarantees
- Audit trails and decision provenance for compliance
Multi-Agent Orchestration for Complex Incidents
- Coordinating specialist agents: triage agent, diagnosis agent, remediation agent
- Inter-agent communication and shared context management
- Conflict resolution when agents propose contradictory actions
- End-to-end major incident simulation with multi-agent response
Observability and Evaluation
- Tracing agent reasoning chains for debugging and audit
- Evaluating agent decision quality: precision, recall, time-to-resolution
- Feedback loops: learning from operator overrides and outcomes
- Cost tracking and token economics for operational agents
Production Deployment and Operations
- Deploying agents as services: APIs, webhooks, and scheduled jobs
- Gradual autonomy rollout: shadow mode to full auto-remediation
- Runbook for agent failures: what happens when the agent itself breaks
- Building the business case and measuring ROI for autonomous operations
Cerințe
- Experience with IT operations, DevOps, or SRE practices.
- Familiarity with Python scripting and REST APIs.
- Basic understanding of LLM capabilities and prompt engineering.
Audience
- SRE and DevOps engineers exploring AI-driven automation.
- Platform engineers building self-healing infrastructure.
- IT operations leads evaluating agentic AI for incident management.
14 Ore
