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Course Outline
Introduction to Agentic AI for Operations
- The transition from static runbooks to reasoning-driven automation in IT environments
- Core components of agent architecture: reasoning cycles, tool execution, memory management, and planning capabilities
- Criteria for determining when to implement automated workflows versus maintaining human oversight
Agent Frameworks and Architectures
- Single-agent operational patterns: ReAct, Plan-and-Execute, and tool-calling loops
- Multi-agent structural designs: supervisor-led, hierarchical, and swarm-based approaches
- Evaluation of development frameworks: LangGraph, CrewAI, AutoGen, and bespoke agent solutions
- Implementation steps for initial operational agents: monitoring queries, diagnostic analysis, and proposed actions
Tool Integration for IT Operations
- Connecting agents to infrastructure monitoring systems via APIs from Prometheus, Grafana, Datadog, and PagerDuty
- Log data retrieval strategies using Elasticsearch, Loki, and Splunk integrations
- Infrastructure management through agent-mediated execution of kubectl, Terraform, and Ansible commands
- Designing secure tool interfaces with rigorous parameter validation and idempotency standards for government systems
Incident Response Automation
- Automated incident triage processes: severity assessment and appropriate routing protocols
- Generation of root cause hypotheses and systematic evidence collection
- Execution of automated remediation tasks, including service restarts, scaling adjustments, rollbacks, and failover operations
- Development of incident runbook agents with configurable autonomy levels for federal agencies
Safety, Guardrails, and Human-in-the-Loop
- Classification of agent actions by risk level: read-only, low-risk, high-risk, and destructive operations
- Establishment of approval gates and escalation procedures for critical infrastructure activities
- Implementation of safety guardrails: action allowlists, blast radius containment, and guaranteed rollback mechanisms
- Maintenance of audit trails and decision provenance to ensure regulatory compliance
Multi-Agent Orchestration for Complex Incidents
- Coordination of specialized agent roles: triage, diagnosis, and remediation functions
- Management of inter-agent communication channels and shared contextual data
- Protocols for resolving conflicts when agents propose opposing remediation strategies
- Conducting end-to-end major incident simulations to validate multi-agent response capabilities
Observability and Evaluation
- Tracing agent reasoning pathways to facilitate debugging and comprehensive auditing
- Assessment of agent decision quality using metrics such as precision, recall, and time-to-resolution
- Implementation of feedback loops to incorporate operator overrides and performance outcomes into model training
- Monitoring computational costs and token economics to ensure efficient use of government resources
Production Deployment and Operations
- Deployment methodologies for agent services, including API endpoints, webhook triggers, and scheduled jobs
- Phased rollout strategies for autonomy, progressing from shadow mode to full automated remediation
- Procedures for agent failure scenarios, ensuring operational continuity when automation systems encounter errors
- Development of business cases and measurement frameworks for return on investment in autonomous operations for government entities
Requirements
- Demonstrated proficiency in information technology operations, site reliability engineering (SRE), or DevOps methodologies.
- Competence in Python programming and the utilization of RESTful application programming interfaces (APIs).
- Foundational knowledge of large language model functionalities and prompt engineering techniques.
**Target Audience**
- SRE and DevOps professionals investigating AI-enabled automation solutions.
- Platform engineers responsible for developing resilient, self-healing infrastructure.
- IT operations leadership assessing agentic AI applications for incident response workflows.
14 Hours
