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A six-month analysis of real adversarial inputs reveals that simple multi-turn setups, forward-momentum exploitation, and role redefinition attacks consistently bypass single-message classifiers. The post argues that stateful monitoring of conversational context is more effective than improving one-shot detection.
Discusses how AI agents for SMB verticals often degrade after launch due to context drift — changes in business operations that the agent doesn't automatically reflect — and suggests solutions like syncing with existing business tools and limiting agent scope.
The article highlights the problem of AI memory becoming unreliable after six months, with contradictions and drifted summaries, and questions whether the industry is focusing on adding more storage rather than improving maintainability.