Prometa AI Quality & Trust Platform
Every agent is evaluated, scored, and controlled before and after production.
Masked data only. Synthetic, historical, and adversarial evaluation in one session.
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Approved by the decision layer. This agent can move into production.
Live Agent Evaluation
Approved automatically because score cleared the production release threshold.
Masked production-like input normalized and aligned to the evaluation scenario.
Revenue Orchestrator generated an output candidate with governed tool access.
Output structure, factual shape, and policy posture were captured for scoring.
Quality score settled at 87 / 100 across relevance, safety, latency, and policy checks.
No blocking defect surfaced. Minor latency weakness remains.
Approve release with drift validation enabled.
Revenue Orchestrator
Revenue Orchestrator completed a governed sandbox run and produced a promotion-safe output.
APPROVED
- latency spike risk
- minor fallback defect
Validated for controlled production release. Keep drift validation active after deployment.
No blocking defect found. Minor latency drag remains under tool fallback conditions.
Quality Map
Track which agents are validated, where defects accumulate, and how quality drops propagate before production is affected.
Defect rate 2.1%
$216K at-risk ARR
Defect rate 2.1%
Defect rate 6.4%
$418K blocked approvals
Defect rate 6.4%
Defect rate 3.7%
$154K service exposure
Defect rate 3.7%
If quality drops
Affected workflows, defect propagation, and estimated impact stay visible before a weak agent reaches production.
Operating Snapshot
A single close-out snapshot shows where agents are safe, where defects are rising, and what to improve next.
Output quality 34% · Latency 29% · Policy 22% · Tooling 15%
Patch Risk Review Agent. It is below the quality gate and needs a safer fallback plus policy repair.
Evaluation Intelligence
This is the defensible layer: evaluation replay, score deltas, and drift analysis tied to actual agent behavior.
Input normalized and masked before evaluation starts.
Agent output compared against expected intent and policy rules.
Score engine measured quality, defects, latency, and readiness posture.
Recommendation issued with drift comparison and improvement guidance.
Threshold sensitivity drift
Blast radius: 1 workflows · 1 integrations · $216k impact
Core Product
Prometa evaluates agents, tracks drift, and blocks weak releases before they reach production.
Evaluation scenarios
Model customer intent, expected output shape, and defect traps as reusable evaluation scenarios.
Drift detection
Baseline previous evaluations, compare new versions, and escalate when output quality or routing drifts.
Quality gate
Blend score, defect likelihood, and policy posture into one production decision layer.