Lazer Technologies released CommerceBench, a benchmark measuring how well AI agents perform shopping workflows in real e-commerce environments. Reading the paper, the system is designed around three components, though only CommerceBench-Eval has been released so far. CommerceBench-Train and CommerceBench-Generator remain on the roadmap.
The motivation is straightforward. Agentic commerce is a hot topic, but there has been no reliable way to measure whether agents are actually “good” at shopping. End-to-end workflows spanning product search, option comparison, constraint handling (price, inventory, shipping), cart management, and checkout initiation are difficult to evaluate with existing web benchmarks. Commerce tasks involve multiple steps, require detecting hidden inventory or shipping logic, and demand adaptation to promotions and stock changes.
Here’s how CommerceBench works. Agents receive structured task programs rather than natural language instructions. These programs specify intent, constraints, and success criteria. For example: “Buy running shoes, under $120, size 10, delivery within 3 days.” If the agent adds the right item to cart and initiates checkout, it succeeds. All executions are deterministically reproducible. Failure types, including wrong option selection, constraint violations, checkout dead ends, and infinite loops, are labeled under a classification taxonomy.
The leaderboard reveals that CommerceBench aims to answer four key questions:
The third and fourth questions are particularly important. Starting with Shopify and expanding to Salesforce Commerce Cloud, Adobe Commerce, and Commercetools means quantitatively comparing agent-friendliness across commerce platforms. And comparing browser-based agents with protocol-based approaches (UCP, MCP) means generating empirical data on the interface layer of agentic commerce.
RWA.xyz published Allocation Vaults: Primer for Institutional Asset Managers, arguing that allocation vaults are emerging as the distribution infrastructure for tokenized assets to reach on-chain capital. Written for institutional asset managers, it’s a useful primer worth unpacking.
Institutional tokenization has progressed through three stages: 1) record on-chain, 2) raise capital on-chain, 3) integrate with DeFi infrastructure. In 2022, KKR partnered with Securitize to tokenize a healthcare fund on Avalanche. The hypothesis was “lower the access barrier, and demand will follow.” Demand didn’t follow.
Demand came from an unexpected place. In 2023, the Fed raised rates to 5.5%. Coinciding with the crypto bear market, DeFi lending rates dropped to around 3%, and on-chain capital flooded into tokenized US Treasuries. Product-market fit was immediate. As of February 2026, tokenized Treasuries exceed $10 billion. Institutional asset managers discovered that blockchain wasn’t an operational efficiency tool but a new distribution channel.
After Treasuries came private credit. But here, problems emerged. Treasuries are liquid and settle quickly, so they work naturally on-chain. Private credit is structurally mismatched with DeFi. Borrowers expect instant liquidity while funds offer quarterly redemptions. Liquidation requires immediate collateral conversion, but illiquid positions must be held and waited on. Transfer restrictions block free movement between smart contracts. Tokenization alone wasn’t enough. The product had to be restructured to fit DeFi infrastructure.
This is where allocation vaults enter the picture. An allocation vault is a smart contract-based allocation vehicle built on top of DeFi lending protocols. Investors deposit USDC and receive ERC-4626 tokens. The vault allocates funds across multiple isolated markets according to parameters set by a risk manager. Yield is generated in each market and flows back to investors.
RWA.xyz lays out the on-chain distribution stack in five layers:
Fasanara’s mF-ONE illustrates how this structure works in practice. Fasanara is a London-based, FCA-regulated private credit manager ($5B+ AUM). Their F-ONE fund operates with traditional liquidity terms: monthly subscription, quarterly redemption. To make it DeFi-compatible, they did three things:
What allocation vaults ultimately do is bridge the gap between tokenizing an asset and creating demand for it. Once accepted as collateral, leverage loops generate demand; demand generates yield; yield attracts liquidity. It’s a self-reinforcing mechanism. Just as distribution has always been the hardest and most expensive problem in traditional finance, it’s the critical challenge on-chain as well. Allocation vaults are an attempt to solve distribution at the infrastructure level.
