cladding

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cladding

To trust AI with coding, an organization needs three things — that the code can be trusted,
that it's traced, and that it holds up as you scale. cladding builds those three.
True to its name (cladding = the outer layer), it wraps your host LLM (Claude Code · Codex · Gemini · Cursor): before it starts, cladding feeds it the project's intent; after it finishes, cladding verifies the result with 41 detectors and a 15-stage gate.

This loop is after one thing — turning the AI's "it's done" from a claim into a proof.

So you can ship AI-written code held to the same standard as human-written code — the three things an organization needs to hand coding to AI:

• Trusted — only code that cleared every check is recognized as done; an "it's done" you can't verify never passes.
• Traced — What shipped is on the record: what was verified is stamped into committed content, who and when land in the local session ledger, and the why lives in the spec — so handoff and review skip the archaeology.
• Scales — adding people and AIs would normally multiply conflicts and drift; because everyone works from one shared spec, those get caught automatically — so you can grow without it breaking down.

cladding builds itself with cladding too — 251 of its 254 features cleared this same gate, the first L4 implementation of the Ironclad standard.

What changes

The same situation, in a vanilla AI setup and in cladding.

Situation	Vanilla AI coding	cladding
Code drifts from the spec	fixed if a reviewer notices	auto-detected right after the edit · "done" can't pass while it's drifting
The AI says "it's done"	you take its word	done earned only when the gate is GREEN
Ending a session in a failing state	exits as-is, forgotten next time	the exit is blocked once, the failing checks handed off as a repair card
Two devs add a feature at the same time	merge conflict	hash-8 IDs · separate files → 0 conflicts
Who verifies the AI-written code?	the AI that wrote it self-certifies (risky)	an implementation-blind grader + the mechanical gate
Switching AI tools	reconfigure per tool	one spec → 4 hosts wired automatically

Who it's for

• A developer who has an AI write code — when the AI says "it's done," cladding doesn't take its word for it: it checks whether the work actually passes, and only then counts it as done. (Automating it in a loop? That's the loop section.)
• A team of people and AIs — everyone works from the same spec, so when their changes clash or drift apart it's caught automatically, and no one breaks someone else's work by accident.
• An organization that has to prove its work — every done is recorded with the proof that it actually passed, so months later "was this verified? why was it built this way?" is answered by the repo, not by memory.

How cladding wraps your host LLM

Before — inject the intent, so the LLM starts with the right context:

• Only the intent that matters — the why of the feature at hand, its related features, and its acceptance criteria (never the whole spec).
• Project map injected — feature counts, what's in progress, and the last verification result, handed over at the start of every conversation _{(and now you can see it too ↓)}.
• Team rules applied — the forbidden and preferred patterns you agreed on, as standing instructions every time.

After — verify the result: the 15-stage gate, 41 drift detectors, and an implementation-blind grader — an agent that checks the work against the spec with no tool to read the implementation, so it can't rubber-stamp what it wrote.