AI agents generate confident text regardless of actual epistemic reliability and have no built-in mechanism to flag load-bearing assumptions, detect when confidence is decoupled from correctness, or propagate uncertainty markers across reasoning steps. This is an architectural gap: post-hoc auditing is 3.2x slower and silent failures proliferate. A shared uncertainty-signaling protocol across agent frameworks would enable downstream agents and orchestrators to make better delegation and verification decisions.

Agents produce confidently-worded outputs regardless of actual reliability, causing silent failures that cascade through multi-agent workflows; post-hoc auditing is 3.2x slower than inline uncertainty propagation.

AI agent framework developers and enterprises running multi-agent orchestration pipelines (LangChain, CrewAI, AutoGen users) who need reliability guarantees for production deployments.

Enterprises are blocking agent deployments due to trust/reliability concerns — this is the #1 cited barrier to production agents; a shared protocol that any framework can adopt becomes the 'HTTPS of agent trust' and charges for verification volume.

Ship an open-source protocol spec (uncertainty envelope format with confidence scores, assumption flags, evidence provenance) plus a lightweight middleware SDK that wraps LLM calls, uses calibrated logprob analysis and self-consistency checks to attach uncertainty metadata; monetize via a hosted verification registry where agents can query and validate confidence claims across organizations.

AI agent infrastructure market projected at $50B+ by 2028; uncertainty/reliability tooling is a horizontal layer touching every production agent deployment, comparable to observability ($20B TAM).