An AI agent is more than a model with a tool list. It is a system that selects actions, observes results, manages state and operates within boundaries. Here is how to design that system so it can make progress without making unchecked decisions.
A practical guide to the path from private input to public verification: define the claim, encode its rules, generate a proof, and decide what the verifier can safely conclude.
Four repositories illustrate four distinct kinds of reusable building blocks: data extraction, ledger access, transaction handling, and local simulation. The useful question is not which project is “best,” but where its boundary sits—and what work remains yours.
A reliable AI application is more than a model call. Follow a request from its user interface through application logic, runtime, external services, observability and deployment—and learn where each boundary deserves attention.
Blockchain infrastructure is easier to design when each layer has a precise job. This guide maps execution, settlement, transport, data access and application tooling into one practical stack.
A practical way to compare AI infrastructure, retrieval systems, evaluation frameworks and learning resources is to ask which part of the workflow each one removes—and which controls and operational burdens it leaves behind.
Wallets, payments and verifiable execution can give agents useful capabilities—but only when the chain solves a coordination problem that ordinary software cannot solve more simply.