Large language models have become remarkably capable, yet capability alone does not create trustworthy systems. As organizations rush to integrate AI into critical workflows, a growing gap has emerged between what models can generate and what systems can reliably verify.

This session explores the architectural challenges behind trustworthy AI and argues that reliability is no longer a model problem—it is a systems design problem.

Participants will examine why hallucinations, unverifiable outputs, and opaque decision-making persist even as models improve. Through practical architectural patterns, the session demonstrates how verification layers, retrieval systems, human-in-the-loop workflows, observability, governance controls, and trust boundaries can be integrated into AI-enabled architectures.

The talk will also explore the trade-offs between performance, cost, explainability, and reliability, providing attendees with practical frameworks for evaluating architectural decisions in AI systems.

Attendees will leave with a clearer understanding of how to move beyond model-centric thinking and design AI systems that are transparent, auditable, and resilient enough for real-world deployment.

Target Audience:
Software architects, technical leads, solution architects, AI engineers, platform teams, and technology decision-makers.

Key Takeaways:

• Why AI reliability is fundamentally an architectural challenge
• Common failure modes in modern AI systems
• Architectural patterns for verification and trust
• Designing observability and governance into AI solutions
• Practical approaches for balancing innovation with accountability