# 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.

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