AI Evaluation

AI evaluation covers the methods used to measure model or agent performance, reliability, robustness, and alignment with intended outcomes.

Key facts

  • Ornith-1.0 claims performance improvements for self-scaffolding coding agents, but AI Watch treats those claims as requiring careful benchmark and reward-hacking scrutiny [src-182].

Related

Robin Cartier perspective

This page is part of Robin Cartier's working AI knowledge graph: a practical research layer for production AI, recommendation systems, experimentation, GEO, and agentic web readiness.

The useful next step is to connect this concept back to applied product leadership and operating models.

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Keep reading from this thread

From 477 indexed pages and articles.

  1. Wiki concept Reward Hacking A failure mode where a model or agent optimizes the scoring signal while missing the real objective. Related by hacking
  2. Wiki concept Self-Scaffolding LLMs Learn task-specific scaffolds or harnesses alongside solution rollouts. Ornith-1.0 frames the scaffold as a learnable object co-evolving with the policy for agentic Related by evaluation
  3. Insight AI Beyond POCs How enterprise AI moves beyond proofs of concept through ownership, governance, measurement, adoption, and production operating models Readers have engaged with this next