Glossary
Component-Aware Scoring
Component-aware scoring is a trust assessment approach that evaluates each individual component of an AI agent's configuration separately, then combines these assessments into a composite score that reflects the full system.
What is Component-Aware Scoring?
An AI agent is not a monolithic entity. It is an assembly of components -- model, prompt, tools, memory, RAG sources -- each of which contributes differently to the agent's behavior and risk profile. Component-aware scoring recognizes this reality by evaluating components individually before aggregating into a composite assessment.
This approach provides several advantages over treating the agent as a black box. It enables more precise score decay when components change (a tool addition affects score differently than a model swap). It helps operators understand exactly which components are contributing to or detracting from their agent's score. And it enables more sophisticated trust thresholds -- a counterparty might accept an agent with a moderate overall score if its individual component scores in the relevant dimensions are strong.
Component-aware scoring also enables predictive capabilities. If an operator is considering swapping their agent's model, the scoring system can estimate the likely impact on each dimension based on the known characteristics of the new model, helping operators make informed decisions about configuration changes before committing to them.
Example
An agent's component-aware score breakdown reveals: Model (Claude Opus 4, strong baseline for reasoning), Prompt (well-structured with clear guardrails, contributing positively to Security), Tools (four integrations, all from verified providers), Memory (persistent conversation history with appropriate retention limits), RAG Sources (curated legal database with quarterly updates). The scoring system identifies that the RAG sources have not been updated in five months, which is dragging down the Quality dimension.
How Signet addresses this
Signet's scoring system is fundamentally component-aware. The configuration fingerprint is built from individual component hashes, and score decay is calibrated per component type (model swap: 25%, prompt update: 10%, tool change: 8%, memory change: 5%). This granularity allows Signet to provide operators with specific, actionable feedback about which components are affecting their score and how proposed changes would impact their rating.
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