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Duration 14 hours
Course Outline
Overview of Agentic AI in Operational Contexts
- Transitioning from static procedural documentation to cognitive agent frameworks within IT automation strategies.
- Structural components of agents: reasoning cycles, tool utilization, memory retention, and strategic planning.
- Criteria for determining automation viability versus maintaining human oversight in operational workflows.
Agent Frameworks and Structural Models
- Single-agent configurations: ReAct, Plan-and-Execute, and tool-invocation loops.
- Multi-agent structures: supervisory, hierarchical, and swarm-based coordination patterns.
- Framework assessment: LangGraph, CrewAI, AutoGen, and bespoke agent solutions.
- Construction of initial operational agents: monitoring inquiry, diagnostic analysis, and remediation proposal.
Tool Integration for IT Operations
- Connecting agents to Prometheus, Grafana, Datadog, and PagerDuty API endpoints.
- Log interrogation via agents: integration with Elasticsearch, Loki, and Splunk environments.
- Infrastructure command execution: kubectl, Terraform, and Ansible through agent actions.
- Designing secure tool interfaces incorporating parameter validation and idempotency standards.
Automation of Incident Response Processes
- Automated incident triage: severity classification and directional routing.
- Generation of root cause hypotheses and systematic evidence collection.
- Automated remediation measures: restarts, scaling adjustments, rollbacks, and failover executions.
- Development of incident runbook agents featuring graduated levels of autonomy.
Safety Protocols, Guardrails, and Human Oversight
- Categorization of actions: read-only, low-risk, high-risk, and destructive operations.
- Establishment of approval gates and escalation protocols for critical operational tasks.
- Guardrail implementation: action allowlists, blast radius constraints, and rollback assurance mechanisms.
- Maintenance of audit trails and decision provenance to ensure compliance for government contexts.
Multi-Agent Orchestration for Complex Incidents
- Coordination of specialized agents: triage, diagnosis, and remediation units.
- Management of inter-agent communication and shared contextual data.
- Resolution of conflicts arising from contradictory agent proposals.
- Simulation of major end-to-end incidents utilizing multi-agent response strategies.
Observability and Performance Evaluation
- Tracing agent reasoning chains to facilitate debugging and audit requirements for government applications.
- Assessment of agent decision quality: accuracy, completeness, and resolution timelines.
- Feedback mechanisms: incorporating operator overrides and outcome data for continuous improvement.
- Tracking costs and analyzing token economics for operational agent deployments.
Production Deployment and Operational Management
- Deployment of agents as services: API integration, webhook configuration, and scheduled job execution.
- Progressive autonomy implementation: transitioning from shadow mode to full automated remediation.
- Operational runbooks for agent failures: protocols for when the agent system experiences disruptions.
- Construction of business cases and measurement of return on investment for autonomous operations.
Requirements
- Practical experience with IT operations, DevOps, or SRE methodologies.
- Proficiency with Python scripting and REST API interactions.
- Fundamental understanding of LLM capabilities and prompt engineering techniques.
Target Audience
- SRE and DevOps engineers investigating AI-driven automation solutions.
- Platform engineers developing self-healing infrastructure systems.
- IT operations leads assessing agentic AI for incident management for government contexts.
