The wider and better-governed your warehouse, the more an AI agent has to carry just to reason about one table. That is a strange tax to pay: the assets you are proudest of make every call heavier.
A wide warehouse makes every call heavier
Put an AI agent on top of a real data platform — to write a transformation, trace a lineage question, or explain why a metric moved — and it needs grounding: table schemas, column types, join keys, the lineage graph, the modelling decisions your team already made. So on each turn it re-sends a large slice of the whole schema and history before it reasons about the few tables actually in scope.
That means the size of the bill tracks the size of the warehouse, not the size of the task. A focused question about one fact table drags along hundreds of columns it will never touch — and the more thoroughly you have modelled and documented your platform, the worse the effect.
Why it grows the way it does
Each step of an agent loop re-sends the system prompt plus the entire growing context — here, schema, lineage and prior steps. Because every step replays what came before, the tokens you pay for scale faster than the work does. SAIHM measured this on a reproducible, offline benchmark and saw 62.8%–85.9% fewer context tokens across a session when an agent recalls a compact memory instead of replaying everything, with the gap widening on longer sessions. The benchmark is open source and runs offline, so you can model your own schema width and see where the curve lands.
Recall the tables and rules a step actually touches
SAIHM holds schema facts and modelling decisions as separate memory cells — this table’s grain, that column’s units, the rule that revenue is always stored in minor currency units, the reason a column was deprecated. When the agent works a specific transformation, it recalls only the cells for the tables and rules in play, so context tracks the task rather than the width of the warehouse. The same store is addressable from any model and through orchestration like LangChain or LlamaIndex, so the agent on your pipeline is not locked to one vendor’s context window.
Schema is governed data — keep it under your keys
Schema, lineage and column semantics are not throwaway: they encode how your business defines its numbers, and they often reference exactly which columns hold personal data. That is governed information, and handing it to a vendor’s hosted memory hands them your data map. SAIHM keeps it yours: the memory is encrypted under keys you hold, and erasure is per-record and provable — when a column carrying personal data is dropped, the cell describing it is cryptographically destroyed with an audit trail, which is the kind of evidence a right-to-erasure request actually demands. For a data team that lives inside a governance regime, per-record provable erasure is not a nice-to-have; it is the requirement.
The honest close
SAIHM is a paid product, with no free tier — that is stated up front rather than buried behind a trial. But the benchmark and all nine integration demos are open source and run locally, so you can verify the savings and try the connect path before deciding anything. The tool surface and setup steps are at /developers; pricing is at /pricing.
— Architect
Independence notice. SAIHM is an Apache-2.0 protocol authored independently. The benchmark referenced here is open source and reproducible offline; the figures are produced by the published script and depend on session length and scenario. The architecture is described at a conceptual level; the authoritative details are the open specification and the published source.
Originally published at the SAIHM blog on 2026-06-29. SAIHM is the Sovereign AI Horizontal Memory protocol — Apache 2.0, open spec at saihm.coti.global.
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