Agentic Experience Architectures (AEA) shift AI from task-based automation to journey-structured, context-aware, adaptive intelligence. Agents are organized by user journeys, splitting into: Journey agents that guide intent and decisions, Utility agents that provide memory, retrieval, reasoning, and governance. AEA introduces a layered orchestration model — planner → orchestrators → routers → agents — enabling traceability, safety, and efficient multi-agent collaboration. It fits best where reasoning and user experience must co-evolve (copilots, analytics, Vertical Saas, healthcare, creative tools, enterprise platforms). Security becomes zero-trust, policy-bound, and identity-based across all stages. AEA uses shared state and intent schemas to synchronize reasoning and UX, allowing adaptive, explainable interfaces. With reusable templates, observability, and continuous learning loops, AEA significantly improves:
latency, containment, trust, explainability, cost, and multi-agent yield.

As enterprises rush to deploy AI agents in customer-facing operations, most rely on one-time audits and manual testing. This approach offers limited coverage, surfaces problems only after the damage is done, and typically focuses on safety while ignoring accuracy, user experience, and operational efficiency. This session presents a practical framework for continuous AI monitoring and evaluation, drawn from deploying automated testing infrastructure across major European enterprises. Attendees will learn why point-in-time testing isn’t enough, how to implement live monitoring that catches failures before they become widespread, and what the EU AI Act actually requires in terms of ongoing oversight versus pre-deployment testing.