Crypto for AI Agents: A New Design Assumption
A quiet narrative shift is happening in Web3: leading infrastructure teams now argue that crypto is fundamentally built for machines. Alchemy co‑founder and CEO Nikil Viswanathan recently said that cryptocurrency networks match how autonomous AI agents operate far better than traditional finance does. Legacy systems are constrained by geography, banking hours and human identity checks, while agents are borderless, always‑on and purely digital. Instead of credit cards or bank accounts, these agents can hold private keys, sign transactions and interact directly with smart contracts. This framing is pushing developers to design blockchain AI infrastructure with software, not humans, as the primary user. In this view, blockchains become settlement and coordination layers for swarms of autonomous AI agents, from trading bots to logistics optimisers, that continuously move value and data without needing a bank manager – or a person – in the loop.
Teneo Protocol and the Open‑Web Backbone for Agents
Teneo Protocol is positioning itself as part of this backbone for autonomous AI agents by solving a key bottleneck: on‑chain execution. While today’s large language models can write code or emails, they struggle to reliably navigate real‑time financial systems. Teneo introduces x402 as a standard for native payment systems, giving agents autonomous transaction processing that does not require human supervision. According to the project, its infrastructure has already handled more than 1,500,000 agent requests, signalling rising demand for crypto for AI agents in decentralized environments. Crucially, Teneo is chain‑agnostic, operating across networks like Avalanche, Base, BNB Chain, X Layer and Peaq. This multi‑chain reach lets agents access data and value across disparate systems, supporting DePIN and decentralized AI use cases. Its integration into CoinMarketCap Labs is meant to boost liquidity and visibility as markets prepare for a future where a significant share of trading and data queries is generated by autonomous AI agents.
Nava AI and the Rise of Autonomous Financial Agents
If Teneo focuses on letting agents act, Nava AI is focused on making sure those actions are safe. The company has emerged from stealth with USD 8.3 million (approx. RM38.4 million) in seed funding led by Polychain Capital to build a verification layer for autonomous financial agents operating in DeFi. As these systems handle trading, lending and staking without human oversight, small prompt errors or malicious manipulation can trigger unintended, costly transactions. Nava’s architecture separates intent from execution: an agent proposes a transaction, Nava’s system independently verifies it, and only then is it executed via an escrow mechanism. Each decision is logged as encrypted traces onchain, creating an auditable history and a feedback loop to improve detection of mistakes and abuse. Beyond verification, Nava aims to become a broader financial infrastructure layer for agents, including payments, chargebacks, insurance tools and a dispute‑aware stablecoin, NavaUSD.
From DeFi Trading Bots to Self‑Driving Treasuries
Put together, projects like Teneo and Nava sketch a future where autonomous financial agents run complex strategies over blockchain rails. Imagine DeFi trading bots that not only execute pre‑set rules, but also call external data, compare yields across chains and self‑rebalance positions, while a verification layer checks that each trade matches predefined risk limits. Corporate or DAO treasuries could delegate routine operations—payroll, bill payments, liquidity provision—to agents that interact with multiple networks through chain‑agnostic infrastructure. Cross‑exchange arbitrage could be handled by agents continuously scanning prices, moving collateral and settling trades around the clock. In all these scenarios, AI agents are the primary users of blockchain AI infrastructure, with humans setting goals and constraints rather than clicking “confirm” on every transaction. The challenge is ensuring these agents remain aligned with human intent when they can move real money at machine speed.
What This Could Mean for Malaysia’s Future Finance
For Malaysian readers, the agent‑native shift raises both opportunities and policy questions. In time, ringgit‑pegged stablecoins could allow autonomous AI agents to transact in a familiar unit of account while using global blockchains as settlement rails. Retail investors might rely on AI co‑pilots to manage diversified crypto portfolios, interact with DeFi protocols or run conservative yield strategies, while SMEs use agents for automated invoicing and cross‑border payments. At the same time, regulators in Malaysia and the wider region will need to consider how existing rules on e‑money, digital assets, KYC and consumer protection apply when the primary actor is an algorithm. Verification layers like Nava’s, and execution frameworks like Teneo’s, hint at how safeguards, auditability and dispute resolution could be built into autonomous financial agents. The key policy debate will be how to harness innovation without letting self‑driving finance create new systemic and consumer risks.