Vision & roadmap

# Building the Governance Layer
for AI-Assisted Development

Mneme is evolving from local governance tooling into the governance infrastructure layer for AI-assisted software development. As AI coding workflows mature, engineering teams will need more than prompt files to maintain architectural consistency at scale.

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Strategic phases

## Where we are. Where we're going.

Phase 1 Current focus
OSS Developer Wedge

Architectural governance for individual developers and early engineering adopters. Mneme starts where the problem is most visible — individual AI coding workflows — and builds the foundation that team and enterprise governance will run on.

- Architectural decision retrieval at prompt time
- Constraint injection into coding workflows
- Governance benchmark validation
- Cursor Rules and CLAUDE.md integration
- CI enforcement gate for AI-generated code
- Deterministic keyword scoring — no ML dependencies

Phase 2 Planned
Team Governance Layer

Shared governance for engineering teams adopting AI-assisted development at scale. The same enforcement model that works for one developer, extended to coordinate decisions across a team without manual synchronization.

- Shared policy and decision stores
- Centralized rule management and versioning
- Team synchronization and conflict resolution
- Organization-wide governance controls
- Role-scoped decision visibility

Phase 3 Planned
Agent Platform Integrations

Governance for enterprise agent workflows. As organizations deploy managed coding agent platforms, governance moves from the individual session to the platform level — enforcing constraints across every agent invocation.

- Managed coding agent platform integrations
- Enterprise AI development environment support
- Internal engineering agent system hooks
- Agent orchestration framework governance
- API-level constraint enforcement
- Workspace execution surface governance — Notion and similar platforms where agents read, write, and trigger workflows

Phase 4 Planned
Governance Infrastructure

Constraint infrastructure for AI software development at the organizational level. Governance becomes a first-class infrastructure concern — not a tool bolted onto a workflow, but a layer that every coding agent runs against.

- Policy-as-code for engineering agents
- Drift analytics and adherence reporting
- Cross-agent governance enforcement
- Workflow and CI/CD governance hooks
- Architectural audit trails across teams

Our thesis

## Why governance is the next engineering bottleneck.

01

AI-assisted development increases code generation throughput faster than reviewer bandwidth. The output volume problem is already here.

02

Architectural review is becoming the next engineering bottleneck. Senior engineers spend their capacity on decisions that were already made.

03

Mneme exists to move governance upstream — from post-generation review to pre-generation architectural constraint. Catch drift before the code exists, not after it reaches review.

How this connects to what's shipped today

## The roadmap is not speculative.

Each phase builds on something already in production. The OSS developer wedge ships today as the open-source `mneme` CLI, the Claude Code hook, the Cursor rules export, and the GitHub Actions enforcement gate — see the integrations index. The architectural argument behind why this layer has to exist outside any single AI coding tool is in the heterogeneous-agents article.

The team-governance phase extends the same decision corpus into shared org-wide policies with deterministic precedence resolution — the design pattern is described on the platform engineering page. The agent-platform-integrations phase aligns with the cross-tool standards forming around AGENTS.md, the Model Context Protocol, and NIST CAISI; we track that landscape on the standards page and the public benchmark methodology behind enforcement claims is at /benchmark/.

The governance-infrastructure phase is the long-term bet: that policy-as-code and drift analytics across engineering organizations will become an operational requirement, not a nice-to-have. That bet is informed by the deployment-quality argument and the regulatory direction NIST CAISI is taking. Phases ship in order; nothing later is gated on the discovery work.

Roadmap reflects current strategic direction and may evolve based on user feedback and adoption.