Integration · Microsoft Agent Forge

# Architectural Governance for Microsoft Agent Forge

Microsoft Agent Forge helps enterprises operationalize autonomous AI agents with managed orchestration, tool calling, and runtime infrastructure. Mneme adds deterministic architectural governance and verification before autonomous workflows modify production systems.

View Governance Benchmarks GitHub Repository

## The gap in agent infrastructure

Agent runtimes optimize execution. They do not enforce architectural invariants.

As autonomous systems gain access to repositories, APIs, CI/CD, enterprise systems, and long-running workflows, the operational risk shifts from generation quality to governance integrity. The model is no longer the weakest link; the workflow is.

Mneme helps enforce:

- ADR-backed constraints — decisions encoded as machine-evaluable rules, not paragraphs
- Repository policies that travel with the codebase, not the policy team
- Architectural boundaries — service, layer, and module separation
- Anti-pattern prevention before non-compliant code lands
- Deterministic verification rules — same constraint, same state, same verdict

All of it applied before autonomous changes propagate downstream.

## How Mneme fits into the stack

Mneme is not another orchestration framework, memory layer, or agent runtime. It operates as a governance and verification layer across autonomous execution surfaces — including Agent Forge.

Layer | Example
--- | ---
Models | OpenAI, Anthropic, Gemini
Agent infrastructure | Microsoft Agent Forge
Execution surfaces | Cursor, Claude Code, Copilot
Governance layer | Mneme
Enterprise systems | GitHub, CI/CD, Jira

Mneme operates as a governance and verification layer across autonomous execution surfaces. The same compiled constraints reach every agent and tool that touches the repo.

## Why governance matters for autonomous workflows

### Runtime observability is not architectural governance

Logs and traces explain what an agent did. They do not constrain what it is allowed to do. Observability is forensics; governance is infrastructure.

### Autonomous execution increases drift risk

Each additional autonomous surface is another path through which architectural constraints can fail to propagate. The blast radius of a single drifted decision scales with how many agents share the codebase.

### Multi-agent systems amplify policy propagation problems

One agent plans, another implements, another reviews, tools mutate state independently. Without a shared, executable governance layer, architectural intent fragments across handoffs.

### Verification becomes more important than prompt quality

Prompts are inputs. Verification is the contract. As model quality saturates, the differentiator is whether outputs can be deterministically checked against architectural intent.

Enterprise AI systems fail gradually through architectural drift long before they fail catastrophically.

## Example governance checks

Dependency policy

### Prevent forbidden dependency usage

Block unauthorized frameworks or libraries from entering autonomous code changes — before the diff lands.

ADR enforcement

### Enforce ADR-backed architectural decisions

Validate generated changes against repository decision records before merge, with provenance back to the originating ADR.

Boundary protection

### Protect service boundaries

Prevent agents from introducing prohibited cross-layer coupling, leaky abstractions, or unintended internal imports.

Workflow constraints

### Validate workflow constraints

Ensure autonomous workflows comply with repository-native governance policies on every step, not only at PR review.

## Governance before generation

Most AI governance systems focus on moderation, compliance, observability, and post-execution review. Mneme focuses on the other side of the call:

- Architectural governance — the rules that define what good looks like in this codebase
- Deterministic enforcement — same constraint, same state, same verdict, every run
- Verification before propagation — checks run before changes reach downstream systems
- Repository-native policy — constraints live next to the code they govern

Observability explains what happened. Governance constrains what is allowed to happen.

## Compatible workflow types

Mneme’s governance layer is workflow-shape agnostic. Common patterns it sits inside:

Autonomous coding workflows Multi-agent orchestration CI automation PR generation systems Agent-assisted refactoring Runtime tool execution pipelines Long-running agent sessions

## Why this matters strategically

As agent infrastructure standardizes, orchestration becomes less differentiated. The next enterprise challenge becomes operational trust, architectural consistency, and deterministic execution governance.

Agent infrastructure enables autonomy. Architectural governance makes autonomy operationally sustainable.

For the broader market-structure read, see the companion essay: Microsoft Agent Forge Signals the Next Layer of Enterprise AI Infrastructure.

## Explore deterministic governance for AI-assisted development

Open-source. Repo-native. Works across Microsoft Agent Forge, Claude Code, Cursor, Copilot, and CI — the same compiled constraints, everywhere the codebase is touched.

GitHub Governance Benchmarks How Mneme Works