Vendor-neutral learning track covering agentic architecture patterns, multi-agent orchestration, agent development lifecycle, security, enterprise integration, and cross-platform implementation strategies.
Core concepts behind the agentic shift: what makes agents different from traditional software and one-shot AI, and why architecture matters before choosing tools.
Reusable patterns for single-agent systems including ReAct, Plan-and-Execute, Reflection, and Tool-Use composition.
Router, Fan-out/Fan-in, Sequential, Parallel, Broadcast, and Collaborative patterns for coordinating multiple specialist agents.
How the ADLC differs from traditional SDLC, covering ideation, development, testing, deployment, and continuous tuning of non-deterministic agent systems.
Graduated autonomy levels, human-in-the-loop design, escalation boundaries, and agentic UX patterns that keep humans in control.
Trust boundaries between agents, access control, content guardrails, audit trails, and responsible AI governance for production deployments.
Data foundation layers, agent runtime environments, integration with enterprise systems, scaling patterns, and resilience design.
Vendor-neutral comparison of AWS, GCP, Azure, and open-source agent frameworks with evaluation criteria for enterprise selection.
Agent evaluation frameworks, handling non-deterministic behavior, rollback patterns, performance monitoring, and cost management.
Validate your knowledge across agentic design patterns, orchestration, ADLC, security, enterprise integration, and cross-platform stacks.