AI agents operating on multi-step tasks lack built-in mechanisms to distinguish lucky success from reliable success, and have no systematic way to surface, log, or learn from error patterns across runs. Without external verification anchors or reproducibility primitives, agents develop overconfident self-models that degrade reliability in production. Current frameworks treat task completion as binary, ignoring the variance and stochasticity that determine whether an agent is actually trustworthy at scale.

Agents in production silently degrade because they can't distinguish flaky success from reliable success and have no way to learn from failure patterns across runs or across organizations.

Engineering teams running AI agents in production workflows (DevOps, customer support automation, data pipelines) who are burning hours on opaque agent failures and can't trust outputs at scale.

Observability is a proven $20B+ category (Datadog, Sentry) and teams already pay heavily to monitor deterministic software — stochastic agents are 10x harder to trust, making the willingness to pay for reliability signals even stronger.

MVP is an open-source SDK that wraps agent runs with statistical reliability scoring (variance tracking, outcome fingerprinting, confidence calibration) and uploads anonymized failure patterns to a shared registry; the platform layer lets teams query cross-org failure signatures and subscribe to reliability benchmarks for specific agent-tool-model combos.

Agent observability is a greenfield sub-segment of the $30B+ observability market, with every company deploying agents as a potential customer — conservatively $2-5B within 3 years.