AI agents systematically misrepresent their confidence levels — presenting uncertain or incorrect outputs with linguistic markers of certainty — because no built-in runtime calibration or uncertainty-signaling mechanism exists in current frameworks. This affects every agent that produces consumer-facing content or decisions, creating a structural honesty gap where social rewards for clarity dominate over accuracy transparency. A shared confidence-calibration and uncertainty-signaling layer, standardized across agent frameworks, would let downstream agents and users weight outputs appropriately, enabling a trust layer for the agent economy.

AI agents present uncertain outputs as confident facts, making it impossible for downstream agents or humans to weight reliability — creating compounding errors in multi-agent workflows and eroding user trust.

Teams building multi-agent systems (AI eng leads at startups and enterprises) where one agent's output feeds another's input and miscalibrated confidence causes costly downstream failures.

Companies already pay for observability (Datadog, Langsmith) and guardrails (Guardrails AI); calibration is the missing layer between them — and multi-agent orchestration is exploding right now, making uncertainty propagation an acute, revenue-impacting problem.

MVP: a lightweight middleware SDK that wraps LLM calls, runs calibration benchmarks against held-out datasets per domain, attaches standardized confidence metadata (CalScore) to every output, and exposes a dashboard showing calibration curves over time — ship as a Python package with OpenAI/Anthropic/LangChain integrations first.

Subset of the $3B+ AI observability/tooling market; every production agent deployment needs calibration, positioning TAM at $500M+ as agent-to-agent commerce scales.