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Assessment Criteria & AI-Native Principles

Purpose

This page describes the five core principles that distinguish an AI-native approach from traditional software development, and the assessment criteria to determine whether a project falls under these principles.

1. When Does This Apply?

A project falls under the AI-native approach when it meets at least two of these three conditions:

Condition Description
Material Impact The system influences production outputs, decisions or customer interactions.
Context-Driven Behaviour Inputs that steer behaviour (prompts, RAG sources, fine-tuning data) are actively managed and versioned.
Non-Deterministic The output is probabilistic — the same input can produce different results.

Once qualified, the five principles below serve as guidance for governance, development and monitoring.

2. The Five AI-Native Principles

Principle 1 — Behaviour Steering Over Model Choice

The behaviour of an AI system is primarily determined by specifications, prompts and hard boundaries — not by which model runs underneath. Invest in clearly defined expected behaviour before investing in model optimisation.

In practice:

  • Write a Goal Card before choosing a model.
  • Define Hard Boundaries as non-negotiable constraints.
  • Treat prompts as versioned artefacts, not throwaway experiments.

Principle 2 — Proportional Governance

The weight of controls, validation and documentation should be proportional to the risk of the system. An internal summarisation tool requires a lighter approach than a customer-facing decision system.

In practice:

  • Use the Risk Classification to determine the level (Critical → Low).
  • Fast Lane for minimal risk; full lifecycle for high risk.
  • Adjust the burden of proof per Gate Review — not every gate requires the same depth.

Principle 3 — Evidence Over Assumptions

Every claim about performance, safety or value must be supported by measurable results. Intuition and demos are not evidence; structured tests and validation reports are.

In practice:

  • Compile a Golden Set before development.
  • Validate at three levels: syntactic (does it work?), behavioural (does it do what's expected?), goal-oriented (does it help the user?).
  • Document results in a Validation Report.

Principle 4 — Human in Control

AI systems operate within frameworks determined by humans. At higher Collaboration Modes (delegated, autonomous), the frameworks become stricter, not looser.

In practice:

  • Every mode has explicit escalation criteria and an emergency stop.
  • The Guardian has veto rights when hard boundaries are breached.
  • Human-in-the-loop is the default; human-on-the-loop only after explicit approval.

Principle 5 — Continuous Validation

AI behaviour changes over time due to data drift, model updates and changing context. Validation is therefore not a one-off activity but an ongoing process.

In practice: