Model-independent governance. Architectural rules that live outside any one model, prompt, IDE, or agent runtime. Governance is a repo-native control layer, evaluated by a deterministic engine, that produces the same verdict regardless of which model or harness generated the work.
Concepts 8 min read · May 2026

# Model-Independent Governance

Models change. Agents change. IDEs change. Provider APIs change. Architectural intent should not. Model-independent governance keeps the rules of the system outside the things that turn over — so the next model migration is a runtime change, not a governance rewrite.

By Theo Valmis · Published May 2026

It is a familiar pattern in software infrastructure. Authentication rules do not live inside one HTTP client. Database schemas do not live inside one ORM. Security policies do not live inside one CI runner. The rule has to outlive the tool, because tools turn over and rules are supposed to stay.

AI-assisted development is now reaching the same conclusion about architectural governance. The model the team used in March is not the model they use in November. The IDE they used last quarter is not the one they use next quarter. The agent runtime that runs unsupervised today is not the one that will run unsupervised in a year. If the team’s architectural rules live inside any of those, the rules are coupled to whatever turns over fastest.

## The operational definition

Model-independent governance is the property that architectural rules are not embedded inside one model, prompt, IDE, or agent runtime. Governance lives as a repo-native control layer that:

- is evaluated by a deterministic engine, not by the model’s discretion
- applies at every surface where work happens — pre-tool-use hook, pre-commit, CI gate, PR check
- produces the same verdict regardless of which model or harness generated the change
- is version-controlled with the codebase it governs, not with the tool it was last enforced through

## Five roles, one of them must outlive the others

An AI-assisted development workflow has five distinguishable roles. Four of them are designed to be swappable. Only one of them is the system of record.

Role 01 Model The execution engine. Generates candidates. Will be swapped on the next capability or pricing cycle.
Role 02 Agent The operator. Sequences the work, calls tools, decides retries. Replaced by the next harness.
Role 03 IDE The surface. Where the human watches and steers. Cursor today, the next thing tomorrow.
Role 04 Provider The API the model is hosted behind. Migrates on cost, latency, region, or compliance.
Role 05 Repo The system of record. The codebase, its ADRs, its policies. Outlives all four above.

If governance lives in roles 01–04, the team has to re-encode it on every migration and accept inconsistency in the meantime. If governance lives in role 05, the migrations cost what they should cost — the work of swapping a tool — and the architectural rules survive intact.

The repo is the system of record. The model is an execution engine. The agent is an operator. The IDE is a surface. Governance belongs outside all of them.

## The same verdict, at every surface

Model-independent governance is testable. The test is: does the same proposed change produce the same verdict across every surface that touches it?

Pre-tool hook
Pre-commit
CI gate
PR check

Each surface is wired to the same governance engine consulting the same repo-native corpus. A change that violates ADR-007 at the pre-tool-use hook in Claude Code violates ADR-007 at the CI gate when GPT-driven CI tries to commit the same diff, and violates ADR-007 at the PR check no matter which agent opened the PR. The model and the runtime do not affect the verdict.

## Provider lock-in vs governance lock-in

The conversation about "lock-in" in AI infrastructure usually focuses on the API. That is the visible cost — multi-provider routing, fallback chains, vendor diversification. There is a second, less visible kind of lock-in that is more expensive to discover later.

Provider lock-in
Visible. API-level.

- Which provider hosts the model
- Solved by multi-provider routing
- Cost: medium — SDK rewrites, retesting
- Mostly an integration concern

Governance lock-in
Hidden. Rule-level.

- Where the team’s architectural rules actually live
- Not solved by routing — the rules are model-coupled
- Cost: high — rules must be re-encoded per migration
- A continuity concern across model and tool changes

Teams can address provider lock-in and still carry governance lock-in if the rules are encoded inside one model’s prompts or one IDE’s configuration. Model-independent governance closes the second gap.

## How it differs from adjacent concepts

Versus a CLAUDE.md or AGENTS.md. Those files are model-coupled by design — they live in the runtime’s prompt loading, not in the repo’s enforcement layer. They are useful and they remain useful; they do not produce a deterministic verdict at the CI gate.

Versus an LLM wiki. An LLM wiki is a knowledge surface. Model-independent governance is a control surface. The wiki helps any model read the same context; governance enforces the same verdict regardless of which model read it.

Versus orchestration framework rules. Orchestration constrains the order of work. Governance constrains what the work is allowed to do. They sit at different layers of the AI operating layer — orchestration is model-aware by necessity; governance does not need to be.

Governance should outlive the model. If the rules survive only as long as the runtime that taught them, governance is a feature of the tool, not a property of the system.

## How Mneme implements it

Mneme is a repo-native, model-independent governance layer. Architectural intent is encoded in a version-controlled corpus, retrieved through a deterministic scoping layer before agents generate, and validated at the hook and CI levels after generation. The same corpus and the same engine drive every enforcement surface — Claude Code today, a different harness tomorrow, a different model the day after.

The rules do not live in the prompt. They live in the repo. That is the property that makes them survive the next migration.

## Frequently asked questions

What is model-independent governance?
Model-independent governance is the property that architectural rules are not embedded inside one model, prompt, IDE, or agent runtime. Governance lives as a repo-native control layer that can be applied before generation, during agent execution, at commit time, and in CI — regardless of whether the work was produced by Claude, GPT, Gemini, Copilot, Cursor, or a custom agent.
Why does model independence matter for AI governance?
Models change. Agents change. IDEs change. Provider APIs change. Architectural intent should not. Governance embedded in a single model or prompt has to be re-encoded every time the model is swapped or the IDE is changed. Governance that lives in the repo, with deterministic evaluation, survives all of those migrations because it never depended on them in the first place.
How is this different from prompt engineering or system prompts?
Prompt engineering tunes a single context for a specific model. System prompts apply to the runtime that loaded them. Both are model-coupled and surface-coupled by design. Model-independent governance is repo-coupled: the constraints live with the code they govern, are evaluated deterministically by the same engine, and produce the same verdict whether the run was driven by Claude in an IDE, GPT in a CI pipeline, or a custom agent.
What does model-independent governance look like in practice?
Architectural decisions are encoded once in a version-controlled corpus. The same enforcement engine consults that corpus at the pre-tool-use hook in Claude Code, at the CI gate on every PR, at pre-commit on the developer’s machine, and during multi-agent workflows that use a mix of providers. The runtime changes; the verdict does not.
Is model independence the same as avoiding provider lock-in?
Related but not identical. Provider lock-in is about which API the team can call. Governance lock-in is about whether the team’s architectural rules are portable. Teams can avoid provider lock-in (multi-provider routing) and still have governance lock-in if the rules are encoded inside one model’s prompts or one IDE’s configuration. Model-independent governance addresses the second risk, which is the more expensive one to discover late.

## Related reading

- Governance Infrastructure
- Architectural Governance
- Executable Architectural Intent
- AI Operating Layer
- Verification Contracts
- Architectural Drift
- Models Are Temporary. Architectural Intent Is Not.
- OpenAI-Compatible APIs Are Commoditizing Models
- Works with — the runtimes Mneme governs without depending on any of them