The argument
Data driven keeps the result. Compute driven ships the derivation.
A claim is compute driven when the artifact carries enough — canonical
input, executable semantics, a derived identity and its provenance — for an
independent machine to derive the claim again.
Re-derivation proves fidelity, not correctness. A wrong computation
re-runs perfectly and is still wrong. What it buys is narrower and more
useful than truth: it removes us from the chain. You do not have to trust
that we ran it — you can run it.
It is also what makes an agent a different kind of thing here. Most AI
products hand a model some tools and call the bundle an agent — which names
the parts and not the property. In a world that keeps its derivation, an
agent is a continuing position in that world: a state you can
establish rather than take its word for, the scoped authority associated with
that established state, and a record connecting what it changed to what
permitted the change. Change the model underneath and the definition stops
deciding the identity question for you — it hands you something to test
instead. Did the successor re-establish the state, the authority, the
obligations and the continuity required to carry on? Model + harness
makes swapping the model an identity change by fiat. This makes it an
empirical question, which is the part the usual definition cannot express at
all — and one we have not yet answered.
The invariants a position is established under →
The long version of this argument, with the ladder, the certificate and a
list of the parts we have not earned yet, lives on the T&R site —
because that is the product that has to survive it.
Read it there →