<!--
  Full-page Markdown export (rendered HTML → GFM).
  Source: https://neotoma.io/ca/deterministic-state-evolution
  Generated: 2026-04-28T13:35:46.857Z
-->
# Deterministic state evolution

Given the same set of observations, the system always produces the same entity state regardless of when or in what order they are processed. This removes ordering bugs and makes agent state testable.

## Before vs after

Before: two agents report conflicting values and whichever write arrives last wins. After: both observations are preserved and a deterministic merge rule resolves the canonical value reproducibly.

◆

## CLI example

\# Agent A writes one value
neotoma store --json='\[{"entity\_type":"contact","name":"Ana Rivera","city":"Barcelona"}\]'

# Agent B writes a conflicting value
neotoma store --json='\[{"entity\_type":"contact","name":"Ana Rivera","city":"San Francisco"}\]'

# Deterministic reducer computes one canonical snapshot
neotoma entities search --query "Ana Rivera" --entity-type contact

Late-arriving observations are folded in deterministically. If the merge rule prefers stronger provenance or recency, it behaves identically on replay, which is required for [reproducible state reconstruction](/reproducible-state-reconstruction). See [versioned history](/versioned-history), [replayable timeline](/replayable-timeline), and [architecture](/architecture).