Skip to main content

Architecture Guide

SystemDox as an AI Context Platform

AI coding agents are powerful but context-starved. SystemDox indexes your architecture knowledge and delivers the right context to the right agent at the right time.

Platform Overview

SystemDox sits between your architecture knowledge and your AI coding tools. It indexes requirements, ADRs, checks, and domain knowledge, then delivers relevant context through two channels: MCP Server (real-time) and Static Export (CLAUDE.md, .cursorrules).

Loading diagram...

SystemDox platform architecture: Knowledge → Context Engine → Delivery → AI Tools
1

MCP Server — Real-time Context

The MCP (Model Context Protocol) server is the highest-value integration. AI coding tools connect directly and query for context while the developer is working. The agent gets architecture decisions, checks, specs, and domain knowledge scoped to exactly what it's building.

Available MCP Tools

get_architecture_decisions

Query ADRs by domain. Returns relevant architecture constraints.

get_checks

Query banned and required patterns by language and area.

get_specs

Retrieve Given/When/Then specifications for a feature.

search_knowledge

Free-text search across all indexed documentation.

Loading diagram...

MCP Server flow: AI agent queries SystemDox for context in real time during code generation
2

Static Export — Context Files in Every Repo

For teams that aren't ready for MCP, or as a baseline layer, SystemDox generates repo-specific context files. These are the same files you'd manually maintain — but kept current automatically from your single source of truth.

Generated Files

CLAUDE.md

For Claude Code

.cursorrules

For Cursor

copilot-instructions.md

For GitHub Copilot

Document Types That Matter for AI

Not all documentation is useful for AI agents. SystemDox focuses on the six document types that directly impact code generation quality.

Type Purpose Example
ADRs Architecture constraints ADR-007 (PwebAuthoriser)
Checks Banned/required patterns "Never use Sentry.captureException directly"
Specs Feature behaviour (Given/When/Then) shared-specs convention
Domain glossary Term definitions "Principal = user_id + tenant_id + role + scopes"
Integration contracts API shapes, events Cross-repo dependency map
Runbooks How to deploy/debug "CI_ENABLED variable controls pipeline"

Loading diagram...

Document types flow through the Context Engine to become scoped, relevant context for AI agents

Bidirectional Sync

Unlike static documentation, SystemDox maintains a live, bidirectional sync. AI agents read context and write back discoveries. GitHub commits trigger re-indexing. CLAUDE.md files regenerate when architecture changes. Documentation stays current because the platform maintains it.

Loading diagram...

Bidirectional sync: AI agents both read and write to SystemDox, keeping documentation current

The Closed Loop

The loop runs across the three surfaces rather than beside them. You capture a decision and it becomes a check. Build delivers that check to the agent, the agent writes code against it, and architecture fitness tests verify the result in CI — validation lives inside Build, not in a stage of its own. What survives review is published to Document, where your team and your AI agents both read it.

And when something does slip through, the violation goes back to Capture as a new check with a fitness test attached, so the same mistake cannot recur. That feedback edge is the learning step — a loop closing over all three surfaces, not a fourth one.

Loading diagram...

Capture → Build → Document: each violation becomes a check that prevents recurrence

What Makes This Different

Bidirectional Sync

AI agents don't just read — they write back. When Claude Code creates an ADR or discovers a pattern, it pushes it to SystemDox. Documentation stays current because the AI maintains it.

Context Ranking

Not all docs are relevant to every task. SystemDox scores and filters context based on what files the agent is touching, what domain it's working in, and token budget constraints.

Check Enforcement

Instead of hoping developers read the docs, SystemDox feeds checks directly to the AI. The agent literally cannot ignore "don't use CognitoAuthorizer" because it's in its active context.

Living Specs

Specs in SystemDox become test generation inputs. Given/When/Then specs aren't just documentation — they're executable context that generates tests automatically.

Ready to give your AI agents real context?

Stop manually maintaining CLAUDE.md files across dozens of repos. Let SystemDox deliver the right context to every AI session.