AI Agents Fail in Production for a Boring Reason: Their Data Is Not Immutable, Queryable, or Close Enough
Most agent projects stall not because the model is weak, but because the agent cannot reliably retrieve complete historical context, reproduce decisions, or prove what it saw. The pattern that scales is storage native: persist immutable facts in object storage, version them with table snapshots, and run ephemeral compute that reads directly from the data layer. This makes agent runs auditable, backfillable, and cheaper to operate than long lived stateful services tied to ingestion paths. The money is there. The production gap is still massive. Enterprise generative AI spend tripled from $11.5B in 2024 to $37B in 2025, with roughly half landing in infrastructure and model access depending on how you segment the stack. The point is simple: budgets are moving fast. Sources: Menlo Ventures: 2025 State of Generative AI in the Enterprise . Report PDF . At the same time, enterprise IT leaders are telling KPMG they are implementing or planning to implement AI ag...