Solution Capability · 6.3
Test AI systems against representative tasks, expected behavior, policies, and business outcomes before and after release.
A successful demo is not evidence that an AI system will perform consistently across real questions, documents, users, edge cases, updates, and adversarial inputs.
AI product owners
Engineering and quality teams
Risk, governance and compliance teams
Business subject-matter experts
Operations leaders accountable for service quality
Define the tasks, risks, expected behavior, and measures.
Create a representative evaluation set with approved data.
Combine automated checks, model-assisted review where appropriate, and human expert review.
Test quality, groundedness, task completion, safety, tools, escalation, cost, and latency.
Run regression evaluations before release and on a recurring schedule.
Review failures and feed them into improvement.
Knowledge-answer evaluation.
Agent task and tool-use evaluation.
Document extraction and classification evaluation.
Voice conversation evaluation.
Safety, policy and escalation testing.
Release regression testing.
Azure AI evaluation capabilities where appropriate, custom test frameworks.
Copilot Studio, OpenAI, Claude, and IGNA.
Application telemetry, test data stores, reporting, and review workflows.
Security & Governance
Evaluation data must be approved, protected, representative, and reviewed by appropriate subject-matter owners. Results, thresholds, limitations, and release decisions should be documented and auditable.
A readiness workshop is the fastest way to find out if this is the right starting point.
