Generic AI analytics agents fail on gaming data because they lack domain context - they double-count revenue by mixing IAP with soft currency, inflate cohort denominators with reinstalls, and reduce whale detection to a spend sort. This post breaks down the five most common failure modes and explains why gaming analytics requires a purpose-built agentic architecture with game-domain semantics.
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Most AI agents rely on prompt tuning and rigid workflows. ClarityQ took a different path. This post breaks down how we rebuilt our agent around robust mechanisms - an agent harness, an agentic semantic layer, guardrails, error recovery, and clear stop conditions - to handle uncertainty, recover from failure, and deliver reliable multi-step analysis at scale.
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