The AI ecosystem suffers from a critical engineering immaturity: deploying stochastic models via manual "vibe checks." Operating autonomous agents at enterprise scale requires abandoning ad-hoc observation for strict, distributed systems rigor. This session introduces a deterministic, CI/CD-native evaluation architecture for Agent Skills, shifting from indeterministic to reliable software execution.

By adhering to the formalized capability standards defined by agentskills.io, we will deconstruct the transition from subjective testing to hermetic, code-driven audits. Attendees will learn to engineer scenario matrices that enforce strict cognitive boundaries via negative testing—guaranteeing agents safely reject out-of-scope triggers. We will demonstrate isolating execution within sandboxed environments to capture pristine telemetry: deterministic tool-call structures, system exit codes, and exact token utilization.

Crucially, we address the anti-pattern of relying on "LLM-as-a-judge" for critical path assertions. Instead, we architect a framework grading system invariants via AST parsing and JSON Schema enforcement to achieve instantaneous, hallucination-immune evaluation.

AGNTCon + MCPCon Europe 2026, Amsterdam, Netherlands