As engineering teams transition from single-model assistants to orchestrating networks of tool-using agents (like the one you deployed at Apple ) and complex multilingual generation pipelines, they confront a new class of systemic failures. These LLM-powered architectures behave like distributed systems, not deterministic functions, leading to issues like: inter-agent disagreement, reasoning drift, semantic divergence across locales, and subtle cascade failures that traditional QA cannot detect.

This session introduces AI Quality Engineering (AI-QE), a vital discipline for making LLM-powered systems observable, reliable, and governable at scale. Drawing from real-world deployments in multi-agent orchestration and global localization workflows, we will walk through the telemetry, evaluation methods, and governance patterns required to ensure alignment and robustness over time

Key Takeaways:
1. Distributed Reasoning Observability: Building telemetry and instrumentation to trace reasoning hand-offs across multi-agent workflows, model delegation-loop failures, and measure tool-use effectiveness
2.Inter-Agent Evaluation Metrics: Quantifying collaboration and systemic reliability using metrics like context-retention fidelity, agreement-divergence ratios, and hallucination cascade detection
3. Multilingual Governance: Techniques to prevent semantic drift and cultural inconsistencies across languages and locales, a core challenge in global RAG pipelines.
4. Quality Gates & CI/CD: Implementing quality gates, domain-specific rubric scoring, and LLM-as-judge ensembles that integrate directly into continuous integration and deployment processes.