Your AI agents code fast. This is how they code right.
Architecture decisions made from real-world evidence, then kept honest as your code grows — so your team lead isn't the only memory.
Big technical decisions still run on gut feel.
Instincts earned before AI
What's risky or worth building has shifted under your senior engineers.
The best teams' lessons are public
How Linear, Notion, and Stripe solved it is one search away. Yours isn't reading them.
Every field has AI research but architecture
Markets, legal, medicine have deep-research tools. Architecture is still a chat box.
You can't hire an AI-native architect
That talent market barely exists. Teams default to whoever's free.
Even the right call silently drifts.
AI forgets what you already decided
Every agent starts from scratch, so the same mistakes return every week.
Code drifts from the plan
Design and implementation quietly diverge. Your team lead is the only one who sees it.
Review can't keep up
Still 1+ hour and 10+ violations per change. Your lead becomes teacher and bouncer.
Your AI CTO reads what the world ships today.
Not training data from a year ago — a live knowledge graph that watches what's being built right now.
The voices who build things
Architecture leads with real track records, not AI influencers.
OSS that's actually adopted
Trending repos filtered for staying power, not flash-in-the-pan stars.
Competitors' architecture
Public repos, ARCHITECTURE.md, talks — not the landing pages.
Papers becoming real
arXiv and USENIX, filtered by what's actually being implemented.
(Microsoft Research)"]:::paper impl["⚙️ Microsoft GraphRAG
open source"]:::impl adopt["✓ Linear's RAG
engineering post"]:::adopt voice["💬 dhh thread
graph DBs at scale"]:::voice paper --> impl impl --> adopt voice -.-> paper classDef paper fill:#1d2025,stroke:#3aada4,color:#f5f0e0 classDef impl fill:#1d2025,stroke:#3aada4,color:#f5f0e0 classDef adopt fill:#facc15,stroke:#facc15,color:#0d1117,font-weight:700 classDef voice fill:#1d2025,stroke:#36302a,color:#a6a299
Five steps that keep your code and your plan in sync.
The brain feeds the loop. Decide, capture, review, guard, drift — all five shipped today.
NEXT"]):::brainNext brain -.-> decide brain -.-> principles brain -.-> review brain -.-> guard subgraph loop [ ] direction LR decide["adr decide
SHIPPED"]:::shipped principles["adr principles
SHIPPED"]:::shipped review["adr review
SHIPPED"]:::shipped guard["adr guard
SHIPPED"]:::shipped drift["adr drift
SHIPPED"]:::shipped decide --> principles principles --> review review --> guard guard --> drift drift -.->|loop| decide end classDef brainNext fill:#1d2025,stroke:#3aada4,color:#3aada4,font-weight:700 classDef shipped fill:#facc15,stroke:#facc15,color:#0d1117,font-weight:700 classDef next fill:#1d2025,stroke:#36302a,color:#a6a299
Decide
Researches what real teams shipped and maps every option. You pick.
Principles
Discovers the review angles your team actually uses, confirmed by interview.
Review
Checks a PR against those principles, citing your own file:line as the example.
Guard
Surfaces principles before the agent writes, and blocks bad commits.
Drift
Full-repo audit: how far has reality drifted from the plan?
Why ADR's research is actually trustworthy.
Says "I don't know"
When evidence is thin, it tells you. No invented confidence.
Your context, on every option
"Self-hosted only" or "EU only" annotates each candidate.
Reads peers two ways
OSS through repos and blogs; closed products through Reddit and HN.
Maps the space, no fake winner
Every option gets its strengths, weaknesses, and gaps. You decide.
Each option ships its own rules
Pick A and your agent gets A's rules. No accidental mix-and-match.
Every claim cites a real source
Each tagged by type — docs, benchmark, or practitioner thread. Nothing on vibes.
An unedited ADR run on a real Beevibe decision.
We ran ADR on our own knowledge-graph decision. 11 candidates surfaced from live research — each with its evidence and gaps, nothing edited.
Open the example report →11 candidates · 85 evidence pieces · 60 citations (57 verified) · 4 Mermaid diagrams · $0.27 · 122 LLM calls · 6 minutes wall-clock.
30 seconds to install. 5 minutes to a research report.
Claude Code plugin or CLI — either works on your existing codebase.
Claude Code plugin
/adr:doctorone-time: API keys
/adr:decidearchitecture deep research
/adr:principleslearn the team's conventions
/adr:review <PR#>PR check vs principles
/adr:guardinstall write + commit hooks
CLI
adr decideresearch a decision
adr principles initdiscover team rules
adr principles refreshre-discover, keeps interview
adr review 42PR-time check (gh/glab/bb)
adr driftfull-repo audit
adr guard installwire the hooks
Also runs as an MCP server (Cursor, Codex, any MCP host). Step-by-step guide → · Full docs →
The story behind it.
The decision layer before the code
Why architecture decisions today still happen in a chat box — and what the workflow should look like instead.
Questions teams keep asking
Why coding agents miss this layer, why frontier models skip it, how ADR reads your internal context.
We tested it on ourselves. It hedged.
Running ADR on a real Beevibe decision and finding it was trying to pick when it should have been mapping. The pivot that fixed it.
Stop letting your code drift from your plan.
Free and open source under Apache-2.0. Make your first decision today; the rest of the loop ships next.