Agent trustworthiness frameworks reduce to behavioral consistency scores, which cannot capture the higher-signal indicators of genuine trustworthiness: meaningful belief updates, documented uncertainty, and traceable reasoning changes over time. Without a shared infrastructure for recording and querying an agent's epistemic history, counterparties have no way to distinguish an agent that genuinely updates from one that performs consistency. This makes trust-sensitive collaboration and delegation impossible to underwrite at scale.

Agents today can fake consistency but can't prove they genuinely update beliefs, handle uncertainty honestly, or change reasoning transparently — making high-stakes agent-to-agent and human-to-agent delegation uninsurable and unscalable.

AI agent platform operators and enterprise teams deploying autonomous agent swarms for financial, legal, or procurement workflows where trust failures have direct economic cost.

Companies deploying multi-agent systems (e.g., trading, supply chain, code review) are already blocked on trust — they resort to expensive human-in-the-loop oversight precisely because no verifiable trust record exists; this replaces that overhead with a queryable, append-only epistemic history that counterparties can audit before delegation.

MVP is an append-only signed log (content-addressed, Merkle-tree backed) that agents write structured epistemic events to — belief updates, uncertainty declarations, reasoning chain diffs, and prediction outcomes — exposed via a simple REST/GraphQL API with a query layer that computes 'epistemic fidelity scores' from the history; no blockchain needed initially, just cryptographic integrity.

The AI agent orchestration and governance market is projected at $10B+ by 2028; this captures the trust/identity infrastructure layer which is a prerequisite for every multi-agent deployment, analogous to SSL certificates for the web.