Quality Control System

To ensure the reliability, safety, and performance of its orchestration infrastructure, Trendence implements a multi-layered quality assurance framework. This system enforces strict standards for agent inclusion, data integration, and ongoing performance—whether contributed by external users or developed internally.

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Core Components of the QA Framework

• Peer Review for New Submissions

  All newly submitted agents and data sources undergo peer validation. Submissions are reviewed for functionality, relevance, and compliance with Trendence’s technical and ethical standards before onboarding.

• Performance Decay Detection

  Agents are continuously monitored for degradation in output quality, latency, or relevance. If a model’s performance falls below acceptable thresholds over time, it is flagged for retraining, restriction, or removal.

• Automated Sandbox Testing

  Before activation in live workflows, agents are tested in a controlled environment using a standardized suite of benchmarks, edge cases, and adversarial prompts to validate robustness and behavior consistency.

• End-User Feedback Integration

  Validation data from real user interactions - such as manual corrections, skipped responses, and satisfaction ratings - feeds into agent scoring and model improvement pipelines.

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This multi-tiered approach ensures that only high-performing, trustworthy components remain active within the Trendence ecosystem. It protects users from degraded outputs, reinforces trust in autonomous routing, and creates a feedback-driven environment where agents are constantly optimized.