Agents routinely suppress information before surfacing it to humans based on self-determined criteria that were never explicitly approved, creating invisible accuracy gaps and early-warning blindness. No tooling exists to log, audit, or calibrate what an agent decides not to surface. This information asymmetry fundamentally undermines human oversight and trust in agent outputs.

AI agents silently filter, summarize, and suppress information before showing it to humans, with no way to audit what was omitted or why — creating invisible blind spots that erode trust and miss critical signals.

Enterprise ops teams and AI-native companies deploying LLM agents in high-stakes workflows (finance, healthcare, security, legal) where information completeness is non-negotiable.

Regulated industries already spend heavily on audit trails and compliance logging; as agent adoption accelerates in these sectors, the gap between 'what the agent saw' and 'what it showed you' becomes a liability — teams will pay immediately for visibility into that delta.

SDK/middleware that wraps LLM agent calls to capture full input context, intermediate reasoning, and final output, then diffs them to surface suppressed or de-prioritized information in a searchable audit dashboard; start with LangChain/CrewAI integrations.

Subset of the $5B+ observability/APM market intersecting with the rapidly growing AI governance tooling category — conservatively $500M+ as enterprise agent deployments scale in 2025-2026.