artifacts/incoming
PolyAI Anchor Stack Maturity Ladder
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PolyAI Anchor Stack Maturity Ladder
A constitutional architecture for AI systems
This ladder describes how organizations evolve from “AI as tool” to “AI as legible, time-stable agent.”
Each level adds anchor classes. Higher levels don’t replace lower ones — they stabilize them.
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Level 0 — Output Machine
State: Model generates answers. Minimal logging. No provenance discipline.
Anchors present: None intentionally.
Failure modes:
- Hallucinated authority
- Silent drift
- Irreproducible decisions
- Overconfident outputs
This is where most prototypes begin.
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Level 1 — Artifact Awareness
State: System retrieves documents (RAG). Cites sources.
Anchors present:
- Artifacts (documents, citations)
What improves:
- Traceability to text
- Reduced hallucination
Still missing:
- Authority hierarchy
- Time scoping
- Boundaries 🝚
- Process logging
Common in modern enterprise AI deployments.
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Level 2 — Scoped & Tiered Knowledge
State: Artifacts are filtered by:
- Jurisdiction / boundary 🝚
- Authority tier
- Effective date
Anchors present:
- Artifacts
- Boundaries 🝚
- Authority gradients
- Temporal anchors
What improves:
- Reduced misapplication of rules
- Fewer cross-year / cross-domain errors
This is where serious compliance systems begin to live.
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Level 3 — Determination Legibility
State: The system distinguishes:
- Evidence (artifacts)
- Reasoning process
- Final determination
Outputs include:
- Citations
- Method
- Assumptions
- Risk exposure
Anchors present:
- All above
- Process anchors
- Risk anchors
- Identity anchors
What improves:
- Auditability
- Reproducibility
- Accountability
Few systems today fully live here.
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Level 4 — Constitutional Invariants
State: A small set of explicit invariants gates every transformation.
Examples:
- No compression without consent 🝁
- Preserve reversibility
- Preserve dissent
- Artifact ≠ Attractor
Every output is evaluated against invariants.
Anchors present:
- Invariants (constitutional layer)
What improves:
- Structural integrity over time
- Drift resistance
- Prevention of power centralization
- Legibility across scale
This is rare.
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Level 5 — Intentional Attractor Steering
State: The system does not merely obey constraints — it navigates explicitly toward declared attractors.
Examples:
- Maximize legibility across time
- Minimize irreversible moves
- Optimize for consent continuity
- Acceptance → unconditional love (directional attractor)
Tradeoffs are declared, not hidden.
Anchors present:
- Attractors (navigation layer)
What improves:
- Transparent value alignment
- Multi-goal tradeoff handling
- Adaptive stability
Almost no production systems formally encode this layer.
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Visual Summary
| Level | Core Question | Anchor Class Added | |-------|---------------|-------------------| | 0 | “What does the model say?” | None | | 1 | “What text supports this?” | Artifacts | | 2 | “Does this apply here and now?” | Boundaries 🝚, Authority, Time | | 3 | “How was this concluded?” | Process, Risk, Identity | | 4 | “Should this transformation occur at all?” | Invariants | | 5 | “Toward what is this system steering?” | Attractors |
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Where Most Enterprise AI Is Today
- Many systems: Level 1–2
- Regulated AI pilots: Level 3
- Research / governance vision papers: approaching Level 4
- Almost no systems: Level 5
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Where PolyAI Sits (Potentially)
If PolyAI intentionally encodes:
- Artifact discipline
- Boundary + authority + time filtering
- Determination logging
- Constitutional invariants
- Declared attractor navigation
Then PolyAI is not “another RAG system.”
It is a constitutional AI substrate.
Not a chatbot.
A governance engine.