Autonomous Operations with AI Agents Training Course

Foundations of Agentic AI in Operations

• The shift from static runbooks to reasoning agents: the evolution of IT automation.
• Anatomy of an agent: reasoning loops, tool utilization, memory, and planning strategies.
• 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 loops.
• Multi-agent architectures: supervisor models, hierarchical structures, and swarm dynamics.
• Framework comparison: LangGraph, CrewAI, AutoGen, and custom-built agents.
• Constructing your first operational agent: querying monitors, diagnosing issues, and proposing solutions.

Tool Integration for IT Operations

• Linking agents to APIs for Prometheus, Grafana, Datadog, and PagerDuty.
• 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 incident triage: severity assessment and smart routing.
• Generating root cause hypotheses and gathering supporting evidence.
• Automated remediation: executing restarts, scaling, rollbacks, and failovers.
• Creating an incident runbook agent with adjustable autonomy levels.

Safety, Guardrails, and Human Oversight

• Categorizing actions: read-only, low-risk, high-risk, and destructive operations.
• Establishing approval gates and escalation policies for critical tasks.
• Guardrail patterns: implementing action allowlists, limiting blast radius, and ensuring rollback capabilities.
• Maintaining audit trails and decision provenance for compliance.

Multi-Agent Orchestration for Complex Scenarios

• Coordinating specialized agents: triage, diagnosis, and remediation units.
• Managing inter-agent communication and shared context.
• Resolving conflicts when agents suggest contradictory actions.
• Simulating end-to-end major incidents with multi-agent response protocols.

Observability and Performance Evaluation

• Tracing agent reasoning chains for debugging and auditing purposes.
• Assessing agent decision quality: analyzing precision, recall, and time-to-resolution.
• Implementing feedback loops: learning from operator overrides and final outcomes.
• Monitoring costs and token economics for operational agents.

Production Deployment and Maintenance

• Deploying agents as services: utilizing APIs, webhooks, and scheduled tasks.
• Phased autonomy rollout: transitioning from shadow mode to full auto-remediation.
• Protocols for agent failures: managing scenarios where the agent itself malfunctions.
• Constructing the business case and measuring ROI for autonomous operations.