Brickken and Taiko to bring Agentic AI tokenization onto Ethereum

Brickken, a tokenization infrastructure solution provider for financial institutions has partnered with Taiko, an Ethereum equivalent Layer 2 network to enable issuers and institutions to create, manager and distribute tokenized assets on their platform in an agentic AI structure.

Brickken chose Taiko because it is built as an infrastructure for tokenized assets.‍ According to the Brickken announcement, for institutions, blockchain selection is not a branding decision. It affects execution costs, compatibility, settlement design, tooling, security assumptions, and the ability to operate assets at scale.

According to Brickken, tokenized financial instruments require predictable infrastructure because they are not static digital objects. They involve investor onboarding, ownership records, compliance checks, reporting, transfer restrictions, corporate actions, distributions, and post-issuance administration.‍

Taiko is an Ethereum Layer 2 designed to give developers the same execution environment as Ethereum, with lower transaction costs and ZK-proven security. Its architecture works with existing Ethereum contracts and tools, so issuers and infrastructure providers can deploy tokenized assets without changing their Ethereum-native development stack.‍‍

A tokenized fund, debt instrument, private credit product, real estate interest, or commodity-linked instrument requires a complete operating layer. Issuers need to onboard investors, verify eligibility, manage documents, enforce transfer restrictions, maintain ownership records, execute distributions, support reporting, and preserve auditability over time. ‍

The Taiko deployment extends that infrastructure into an Ethereum-equivalent Layer 2 environment designed for lower-cost execution and high-frequency on-chain activity.

‍As Ludovico Rossi, CRO of Brickken, said, “Tokenized real-world assets and autonomous agents are converging faster than most infrastructure was built to handle. The combination of Brickken’s tokenization stack and RAMS, our compliance delegation standard for AI agents operating on regulated on-chain assets with Taiko’s Ethereum-equivalent execution environment gives issuers a path to deploy tokenized assets in a context where agentic workflows are a first-class consideration, not an afterthought.”

More important is the introduction of autonomous agents which are introducing a new requirement for tokenized markets. ‍

An agent that holds or interacts with a tokenized asset cannot be treated like an ordinary wallet without context. It needs identity, authorization, scope, and rules. Institutions need to know what the agent is allowed to do, on whose behalf, under what conditions, and within which transfer constraints.

‍This is precisely what RAMS (Regulated Agent Mandate Standard) [ERC-8226] is built for. RAMS is the compliance delegation standard for AI agents operating on regulated on-chain assets. It defines agent authorization, scope, and transfer rules enforced at the protocol level, allowing tokenized assets to operate inside agentic workflows without removing the compliance and ownership controls institutions require.‍

This is where real-world assets and agentic finance begin to converge. ‍ An asset can be tokenized. An agent can transact. But institutional markets require more than transaction capability. They require enforceable rules, verifiable identity, transfer controls, and auditable execution.‍‍

For Brickken, deploying on Taiko expands its multi-chain tokenization infrastructure and gives issuers another environment to deploy and manage tokenized assets through institutional workflows. Taiko’s Ethereum-equivalent architecture, ZK coverage, and low transaction costs make it suited to high-frequency, agent-driven activity alongside real-world asset issuance.‍

Joaquin Mendes, COO at Taiko, added, “The tokenization conversation has been stuck on one number for years: how much you can put on-chain. The number that actually matters now is how much of it can be operated without a human in the loop. That’s a harder problem, and it’s the one we’re building for.”

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