Why AI Agents Act on the Wrong Context

Why do AI agents fail even when the model, prompts, and infrastructure seem correct? I will show how most agent architectures assemble context from fragmented systems—streams, databases, vector stores, and caches—without guaranteeing that the retrieved state ever fully coexisted. In personalization, fraud detection, and multi-agent workflows, this leads to agents acting on incomplete, stale, or conflicting context—especially under concurrency. I’ll break down where these gaps emerge and why scaling amplifies them. You’ll leave with a systems-level pattern for maintaining correct, decision-time context across streaming, relational, and vector workloads.