This module extends observability strategy into the improvement cycle, translating telemetry findings, satisfaction signals, and escalation patterns into prioritized, actionable refinements. You will apply the four-stage continuous improvement cycle framework, practice prioritization reasoning against real telemetry findings, and walk through the full improvement cycle in a realistic weekly telemetry review conversation.

This module develops the testing strategy design skills needed to validate enterprise agentic AI solutions before production deployment. Learners design agent test plans with functional, regression, edge case, and business outcome validation criteria, producing the agent-level testing framework that governs what gets validated before any enterprise AI solution reaches production.

This module extends the agent test plan into the model and prompt validation layer: defining custom model validation criteria, designing prompt evaluation scenarios, and applying Copilot-assisted test case generation to produce a comprehensive prompt test case library. Learners produce the model validation and prompt evaluation components that complete the full testing and evaluation strategy.

This module develops the evaluative skill of systematically applying Microsoft's six responsible AI principles to a real AI solution design: identifying gaps against each principle and assessing how severe those gaps are in a high-stakes deployment context.

This module develops the skill of designing a complete responsible AI governance framework—translating identified gaps in an AI solution's responsible AI review into a structured governance architecture with approval workflows, usage policies, compliance controls, audit logging, and human oversight checkpoints.

This module teaches you to configure comprehensive content safety controls that protect users and organizations from harmful AI outputs and malicious inputs.

This module teaches you to design incident-response processes that detect, classify, and respond to content-safety violations effectively.

This module develops the identity and model security design skills needed for enterprise agentic AI deployments. Learners design authentication and authorization architectures for agents, define scope management controls, and specify model security requirements covering data residency, access governance, and training data protection, applying those skills to a realistic enterprise multi-agent scenario.

This module develops the vulnerability analysis and attack defense design skills needed to protect enterprise agentic AI solutions from prompt-based attacks. Learners analyze the three primary prompt attack categories, map attack paths specific to their deployment architecture, and design a layered set of prevention, detection, and mitigation controls, producing a threat model document that integrates with the security architecture.

This module develops the architectural synthesis skills needed to produce a complete enterprise agent solution architecture package. Learners integrate all program skill domains—platform selection, agent design, multi-agent orchestration, application lifecycle management (ALM), observability, security, and responsible AI—into the four most synthesis-dependent components of an enterprise architecture package: the diagram, security architecture, observability plan, and governance model.

This module develops the architectural communication skills needed to present and defend a complete enterprise agent solution architecture to a stakeholder audience. Learners structure an architecture presentation, identify their most vulnerable decision points, and practice the decision defense skills that distinguish senior architects in client engagements.

Learners produce an integrated operations and governance document for a provided enterprise agent solution synthesizing a monitoring plan, testing strategy, responsible AI review, content safety framework, security architecture, and an executive-facing launch readiness summary into a single coherent operations package that mirrors the operational readiness deliverables a senior AI solutions architect produces before a production launch.