Retrieval-Augmented Generation (RAG) solved the knowledge problem for enterprise AI. But as we move toward agent-driven workflows, a new class of failures is emerging—far more subtle, and far more dangerous - Context poisoning.
In AI-native workplaces, agents don’t just retrieve information—they accumulate, transform, and act on context across documents, APIs, chats, and tools. Over time, this context becomes noisy, inconsistent, and sometimes adversarial, leading to silent failures: incorrect decisions, broken workflows, and loss of trust.
In this session, we explore how context poisoning manifests in real-world enterprise systems—from long-running copilots to multi-step agent orchestration—and why traditional RAG architectures are not enough.
You’ll leave with a mental model and actionable strategies to evolve from static RAG pipelines to reliable, production-ready graph-based agent systems—especially in AI-powered workplace environments like copilots and digital assistants.
Because in the shift from retrieval to reasoning, managing context—not just generating text—is what defines whether your AI system works… or quietly fails.