Mneme HQ is the architectural governance layer for AI-assisted development. It compiles your team's architectural intent — ADRs, service boundaries, security rules, framework constraints — into deterministic, machine-readable constraints that govern coding agents at the pre-generation stage. This is governance before generation: architectural drift prevention applied to AI coding workflows before code is written, not after review. The use cases below apply that governance layer to specific surfaces — coding assistants, multi-agent pipelines, legacy codebases, security and compliance, data platforms, and design systems.
Use Cases

# AI Governance Use Cases for Coding Agents and AI-Assisted Development

Architectural governance reference architectures for AI coding workflows. Enforce ADRs, security boundaries, and engineering standards across Cursor, Claude Code, Copilot, and multi-agent pipelines. All scenarios are simulated.

## What Mneme governs

Architectural governance is broader than a rules file. Mneme HQ compiles six categories of engineering intent into deterministic constraints applied to every AI coding session.

Architectural decisions
ADR enforcement at prompt time. Decision continuity across sessions, repos, and agents.
Security boundaries
PII handling, auth flows, secret access, and compliance controls enforced before generation.
Repository policies
Service boundaries, layering rules, and module ownership made machine-readable.
Framework constraints
Approved libraries, forbidden patterns, and version pins applied to AI-generated code.
Workflow standards
Naming conventions, test scaffolding, and engineering standards enforced uniformly.
Anti-pattern prevention
Architectural drift, prompt-induced regressions, and known bad patterns flagged pre-generation.

## Governance before generation

The governance-before-generation pipeline: architectural intent compiles into deterministic constraints before any AI coding agent generates code. Drift detected post-generation feeds back into the governance layer, preventing the same violation across future sessions.

## Reference architectures

Mneme HQ enforces architectural, security, design, and workflow decisions wherever AI generates code or structured output. These reference architectures show how teams apply pre-generation governance across different LLM-powered workflows.

Reference Architecture

### Coding Assistant Governance

Prevent Cursor, Claude Code, and Copilot from violating architecture rules — session after session. Enforce ADRs, service boundaries, and naming conventions at prompt time.

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Reference Architecture

### Legacy Codebase Memory

Give every coding assistant access to the undocumented rules that only your longest-tenured engineers know. Capture tribal knowledge once — enforce it forever.

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Reference Architecture

### Security & Compliance Guardrails

Enforce PII handling rules, auth requirements, and compliance controls before the LLM generates a single line of non-compliant code. GDPR, SOC 2, and beyond.

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Reference Architecture

### Data Platform Governance

Keep AI assistants aligned with warehouse layer boundaries, naming conventions, and pipeline constraints. Prevent raw table writes and schema drift before code is written.

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Reference Architecture

### Design System Governance

Stop AI from inventing colors, breaking component hierarchy, and ignoring accessibility rules when generating UI. Enforce design tokens and WCAG requirements at prompt time.

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Reference Architecture

### Multi-Agent Workflow Governance

Give every agent in your pipeline — planner, coder, reviewer — a shared memory of architectural decisions. One decision store enforced at every stage.

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### CI Governance for AI-Generated Code

The deterministic gate that catches what reviewer attention can't. Mneme's enforcement checks run against every PR diff in GitHub Actions or GitLab CI — blocking merges that violate architectural decisions, regardless of which agent produced the code.

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Coming up

### ADR Enforcement

Deterministic ADR enforcement for AI coding agents — turning architectural decision records into pre-generation constraints with CI gates and drift telemetry. Dedicated reference architecture in progress.

In progress →

## Works across AI coding ecosystems

Mneme HQ is ecosystem-neutral. The same governance layer enforces deployment governance and engineering standards across every major AI coding agent and orchestration framework.

Cursor Claude Code GitHub Copilot GitHub Actions LangGraph CrewAI OpenAI API workflows

## Mneme is not a generic AI memory system

Memory recalls. Governance enforces. Mneme focuses on deterministic decision continuity for AI-assisted development — not probabilistic recall.

Category | What it does | What Mneme does
--- | --- | ---
RAG memory | Probabilistic retrieval of relevant context chunks at inference time. | Deterministic enforcement of architectural constraints before generation.
AI memory tools | Recall prior conversation context across sessions. | Operationalize architectural decisions as machine-readable rules.
Rules files (Cursor, etc.) | Human-readable standards documented per-repo. | Compiled, validated, version-controlled governance with CI enforcement.
Code review | Catches violations after generation has consumed engineering effort. | Prevents violations before generation — governance before generation.

## Continue reading

Explore the governance cluster: insights on architectural integrity, comparisons against adjacent tools, integrations, and benchmark methodology.

Insight
Why prompt engineering is not governance
Prompts steer; governance enforces. Why architectural integrity needs deterministic constraints.
Insight
Why RAG fails for architectural governance
Probabilistic recall is not enforcement. The structural reason RAG cannot prevent drift.
Insight
Why code review cannot scale with AI output
Linear review against non-linear generation. Governance shifts the constraint left.
Integrations
Mneme across coding agents
Cursor, Claude Code, GitHub Actions, and more — one governance layer, every surface.
Compare
Mneme vs. adjacent tools
Cursor rules, Claude Code memory, RAG, CodeRabbit — where each fits and where Mneme starts.
Benchmark
Governance benchmark methodology
Pass/fail examples and deterministic enforcement measured across simulated AI development workflows.
Roadmap
What's next for Mneme
Scalable AI development with operational consistency and decision continuity at the core.
Founder
Why we built Mneme
The architectural integrity gap in AI-assisted development — and how to close it.

## Common questions

What is Mneme HQ?
Mneme HQ is the architectural governance layer for AI-assisted development. It compiles architectural intent into enforceable constraints that govern AI coding agents at the pre-generation stage, before architectural drift reaches review.
What is AI engineering governance?
AI engineering governance is the discipline of enforcing architectural decisions, security boundaries, and engineering standards on AI-generated code. Unlike code review or prompt engineering, governance operates before generation: it constrains what coding agents can produce, ensuring operational consistency and decision continuity across sessions.
What is architectural drift in AI-assisted development?
Architectural drift is the gradual divergence between an AI agent's generated code and the team's architectural decisions — service boundaries, naming conventions, ADRs, framework constraints. Drift compounds session over session because coding agents have no persistent enforcement layer. Mneme prevents drift by making decisions machine-readable and deterministically enforced at prompt time.
How is Mneme different from RAG memory or generic AI memory systems?
RAG retrieves knowledge probabilistically. Generic memory systems recall context. Mneme is not a memory system: it is a governance layer that enforces architectural constraints deterministically before code is generated. Memory helps recall; governance enforces.
What is governance before generation?
Governance before generation is the principle of constraining AI coding agents at the pre-generation stage — compiling architectural intent into machine-readable rules that flag conflicts before the model produces code. It contrasts with post-generation review, which catches violations only after engineering effort has been spent.
How does Mneme HQ differ from rules files, memory tools, and RAG?
Rules files document standards. Mneme enforces them. Memory tools recall context. Mneme governs implementation. RAG retrieves knowledge. Mneme operationalizes decisions. Each adjacent tool exists for a reason; none of them govern implementation.