For Platform & DevEx Teams

# Centralize Architectural Standards Across AI Coding Workflows

Every team has their own .cursorrules, their own system prompts, their own interpretation of the standards. Mneme HQ gives you one structured decision corpus that all AI agents query, consistently.

By Theo Valmis · Published Apr 2026 · Updated May 2026

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The Problem

## Rules sprawl is the hidden cost of AI-assisted development.

01

#### Every team maintains separate Cursor Rules, CLAUDE.md, and system prompts

Fragmented rule files multiply across repos and teams. No team knows what the others have defined.

02

#### No single source of truth for what the AI "knows" about your architecture

Each agent operates on a different subset of standards. Outputs diverge in ways that are hard to trace back.

03

#### Prompt templates diverge over time: no versioning, no conflict resolution

Templates are copied, modified, and forgotten. There is no audit trail and no way to know which version is current.

04

#### Onboarding new teams to AI coding means re-documenting the same standards

Platform teams repeat the same setup work for every new team. There is no reusable distribution mechanism.

What Platform Teams Get

## One corpus. Every agent. Every tool.

#### Single source of truth

`project_memory.json` is the canonical architecture source, queried by all agents. One file to maintain. One place to update.

#### Scoped rules

Payments team gets payments rules, analytics team gets analytics rules. Per-file pattern matching ensures each agent sees exactly what it needs.

#### Conflict resolution

The precedence engine resolves org-level vs team-level exceptions automatically. No manual arbitration. No ambiguity about which rule wins.

#### Universal enforcement

Claude Code hook integration means you install once and enforce everywhere. Standards propagate to every team without any per-team setup.

Use Cases

## Built for platform and governance use cases.

#### AI Coding Assistant Governance

Enforce architectural standards across every AI coding assistant in your organization from a single decision corpus.

Read use case →

#### Multi-Agent Workflow Governance

Maintain consistent standards across orchestrated multi-agent pipelines where multiple models collaborate on the same codebase.

Read use case →

## How platform teams roll this out

The deployment model platform engineering teams typically settle on has three tiers, each owned by different parts of the org but driven by the same decision corpus.

Tier 1 — org-wide policy. A small set of decisions every repo inherits: approved dependencies, security patterns, observability conventions. Owned by platform / security. Lives in a shared `project_memory.json` imported by each repo's local corpus.

Tier 2 — team architecture. Service boundaries, language and framework choices, data-access patterns. Owned by the team that operates the service. Lives in the repo's own `project_memory.json` with its own ADRs.

Tier 3 — per-feature exceptions. Tracked overrides for specific PRs, with rationale and expiry. Owned by the engineer making the change, reviewed in PR like any other diff. The override is a decision record itself, so weakening a constraint is visible.

Mneme's precedence engine resolves the three tiers deterministically: more-specific scope wins over less-specific scope, more-recent decision wins over superseded decision, explicit override wins for the duration of its expiry. The result is policy-as-code that is queryable by any agent, applied at the seam every agent eventually has to write to disk, and auditable in the same way as the rest of the codebase.

## FAQ

Does this replace our existing policy-as-code tooling?
No. Mneme governs AI-generated code at the moment of generation. Existing policy-as-code (OPA, Sentinel, custom linters) governs deployed configuration and infrastructure. They operate on different artifacts and complement each other — Mneme prevents architectural drift from being introduced; OPA-style tooling prevents misconfigured deployments from being applied.
How does this fit alongside CodeRabbit, Sentry, or post-generation reviewers?
Mneme operates pre-generation; review tools operate post-generation. Both layers are useful: Mneme catches architectural violations before they're proposed, freeing review tools to focus on correctness, security, and judgment calls. See Mneme vs CodeRabbit and Review Is Not Governance.
What does the rollout look like for a 50-engineer org?
Typical sequence: pilot in one repo with the platform team's own ADRs (week 1—2), wire up the GitHub Actions gate in warn mode (week 3), expand to two or three more repos with team-owned tier-2 decisions (month 2), turn the gate to strict on the most architecturally-stable repos (month 3). The cost curve is gentle because the corpus is human-editable JSON, not a custom DSL.