Agent Memory With Provenance, Supersession, and Tri-Temporal Fact History
SurrealDB's Spectron launch pitched agent memory you can trust, and its PH thread did the market research in public: a user wanting to ask why a score changed between analysis versions and getting nothing useful from the storage layer, another stating corrections lost in the memory layer cost you before you notice. Memory that stores corrections as superseding facts with provenance, never overwriting, is the production requirement most agent memory products skip.
Problem Statement
A team's agent tells a customer the old pricing because a correction got overwritten somewhere in a vector store, and nobody can trace when or why the wrong fact won. Current memory layers rewrite latest values, losing the change trail. The Spectron commenters defined the spec: corrections supersede rather than overwrite, every fact carries provenance, and the system answers what changed, when, and which source caused it. Without that, agent memory fails exactly when it matters, silently.
The Idea
A memory layer for production agent teams that records every fact with source, validity time, and supersession chains so agents answer from current truth and humans can audit why answers changed.
Why Now
Agent memory moved from demo feature to production liability in 2026 as long-running agents accumulated stale facts and answered confidently from them. The Spectron thread shows buyers articulating the audit requirement in their own words, and tri-temporal modeling, long established in finance databases, is now being demanded for agent infrastructure.
Target User
Engineering teams running long-lived agents with customer-facing or decision-bearing outputs
Target Market
AI agent infrastructure and memory systems
The full brief is free to read
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- MVP scope & feature boundaries
- Step-by-step validation plan
- Score rationale across 11 dimensions
- Monetization model & pricing angle
- Competitors with links
- Acquisition channels & go-to-market
- Risks & counter-evidence
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