·AI Agent Engineering
Role-Based AI Agents: Enterprise Integration Architecture
Design role-based AI agents that connect safely to ERP, CRM, ticketing, identity and knowledge systems through APIs, permissions, approvals and audit trails.
Technology learning library
Answers you can test in a practical lab.
Use these guides to compare architecture choices, understand delivery risks and prepare for instructor-led labs across our five specialist training tracks.
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Latest practical guides
·AI Agents
AI Agent vs Workflow Automation: Which Should You Build?
Choose between deterministic automation, a single AI agent and multi-agent orchestration using task variability, risk, measurability and operating cost.
·Incident Response
Cloud and AI Incident Response: A Practical Playbook
Plan cloud and AI incident response across identity containment, agent tool misuse, evidence preservation, credential rotation, recovery and control validation.
·Data Contracts
Data Contracts for AI Pipelines: A Practical Guide
Use data contracts to control schema evolution, semantics, quality, ownership, privacy and service expectations across analytics and AI data pipelines.
·Data Observability
Data Observability for AI: Quality, Lineage and Incidents
Monitor AI data pipelines with freshness, volume, distribution, contracts, lineage, ownership and incident response from source to model or retrieval output.
·FinOps
FinOps Unit Economics for Platform Engineering Teams
Connect cloud and platform spending to cost per workload, deployment, transaction or customer while preserving reliability and business value.
·AI Agent Evaluation
How to Evaluate AI Agents Before Production
Evaluate AI agents using scenario sets, task success, tool accuracy, handoff quality, policy compliance, latency, reliability, human review and unit cost.