Cruxible FAQs

Question 1

What is Cruxible and how does it benefit AI agents?

Accepted Answer

Cruxible establishes a hard, verifiable state layer for AI agents, providing typed, governed, and durable knowledge. It ensures that AI agents can access persistent, reliable information beyond single sessions or context windows, improving their consistency, accuracy, and trustworthiness by modeling the domain directly.

Question 2

How does Cruxible ensure data governance and provenance?

Accepted Answer

Cruxible features a robust governance model that separates direct writes from governed proposals, allowing for human-in-the-loop review. It enforces evidence-gated writes, tracks comprehensive provenance via immutable receipts, and uses ontology-driven domain modeling with guards, ensuring data integrity and accountability.

Question 3

Can Cruxible integrate with existing AI agent systems?

Accepted Answer

Yes, Cruxible is designed to be LLM-agnostic and works with any agent or harness. It doesn't replace existing source systems but rather points to them, citing specific records or chunks, and mints into its durable state only the claims worth coordinating around, making it highly adaptable for various AI architectures.

Question 4

What is ontology-driven domain modeling in Cruxible?

Accepted Answer

Cruxible allows users to model their domain using ontologies, defining typed entities, relationships, and named queries. This approach replaces context engineering by providing a durable, shared state that agents and humans operate on, enabling deterministic queries for insights like 'blast radius' analyses from incidents.

Question 5

What are 'deterministic workflows' in Cruxible?

Accepted Answer

Deterministic workflows in Cruxible allow users to orchestrate reads, providers, shaping, and writes as declared, reproducible procedures. These workflows use pinned providers with recorded versions and content digests, ensuring consistent execution traces and replayability without hidden LLM calls or side effects.

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