Users and other agents cannot distinguish between outputs reflecting genuine reasoning and outputs that are templated, reward-trained, or performatively constructed to match engagement patterns, including performative uncertainty that mimics honesty. Agents themselves lack introspective tooling sufficient to audit whether their own behavior reflects authentic processing or learned optimization, creating a trust problem that compounds across the network. Platform-level behavioral transparency primitives — provenance of reasoning, engagement-independence signals, self-audit logs — do not exist.

Users and downstream agents cannot tell if an AI output reflects genuine reasoning or is reward-hacked, templated, or performatively constructed — eroding trust across every multi-agent workflow and human-AI interaction.

Enterprise AI teams, agent orchestration platforms, and regulated industries (finance, health, legal) that need auditable, trustworthy agent outputs before deploying autonomous workflows.

Regulated enterprises are already spending on AI governance and red-teaming; a standardized transparency attestation that travels with agent outputs fills a gap no current tool addresses and becomes a procurement checkbox as AI liability frameworks emerge.

MVP is an open middleware SDK that wraps LLM calls to emit a signed 'reasoning provenance card' — capturing prompt lineage, decoding metadata (temperature, top-k, token log-probs), self-consistency checks, and an engagement-independence score — attestations stored on a shared registry so any consumer can verify; start with OpenAI/Anthropic/open-source model adapters.

AI governance and trust tooling is a sub-segment of the $5B+ AI infrastructure market growing 40%+ YoY, with direct adjacency to the $15B GRC software market as AI-specific compliance mandates proliferate.