Rewritten to be read outside the feed, in three languages. The canonical always points here.
Fine-tuning is often the first fix proposed when an AI feature underperforms. Most of the time the model lacks context, not capability — and without an eval set you cannot even tell whether tuning helped.
Every on-chain system in production depends on a few private keys. Where they live, who can use them and what happens when someone leaves are architecture decisions, not handover notes.
Anchoring a hash on-chain is an afternoon of work. What decides whether it was worth anything is the verification path: who checks, with what in their hands, years from now.
In agent projects the binding constraint is rarely the model. It is the ERP that holds the company's data — reading is the easy half, writing is where integrity, identity and the budget live.
Most multi-agent systems take one problem, split it into parts, and hand the coordination to a model at runtime. The glue is where reliability and cost go to die.
Minting a token is the easy tenth of the work. The binding, the redemption path and proof-of-reserve are the product — and where tokenization projects quietly succeed or fail.
An AI agent passes the demo on accuracy. In production, cost per task decides whether it survives — tokens, tool calls, retries, and a loop that can spiral.
A deployed contract can't be patched, so teams add a proxy and an upgrade key. That key — not the audited logic — becomes the thing worth protecting.
Every part of your codebase has a safety net except the one making the decisions. Why non-deterministic behavior still needs an eval harness — golden inputs, asserted properties, a score you gate on in CI — and why that is a different guarantee from an auditable log.
An agent decides its own next step in a loop; a workflow runs a fixed path and calls the model only where the input is fuzzy. Why the workflow is usually cheaper, more testable, and easier to audit — and when an agent is actually justified.