Thesis
Intelligence is the arbiter of power, culture and the means of life on earth.
Despite its pervasive influence, a precise definition of intelligence remains elusive. Most scholars, however, emphasize the incorporation of learning and reasoning into action. This focus on the integration of learning and action has largely defined the field of Artificial Intelligence, with some early researchers explicitly framing the task of AI as the creation of intelligent agents capable of “perceiving [their] environment through sensors and acting upon that environment through effectors.”2
Yet the current environment in which these AI Agents operate is fragmented and almost exclusively defined by private interest. These dynamics impede the potential for societal value through collective experimentation and innovation. Such an approach requires an open, persistent environment with self-enforcing standards and a shared medium for communicating and transacting. Without such an environment, Agents will remain gated and constrained private instruments, trapped in designated niches and narrow interest groups.
Thankfully, Web3 has been building this vision of an immutable and shared digital environment for nearly a decade. However, the value it has created is largely limited due to several pressures:
risky, high-stakes user interactions
“skinny interfaces” dictated by smart contract constraints
learning curve reset with each new primitive
To transcend these pressures and the adoption bottleneck, Web3 needs a new interface paradigm: one which prioritizes user preferences and abstracts the complexities involved in interacting with intricate technical and financial logic.
We see Agents as an emerging intermediary layer capable of serving diverse functions, user requirements and risk appetites. Agents can manage the inherent risks and complexities of smart contract applications in a verifiable and traceable manner, enabling automated risk management, smart assets, decentralized insurance and many more context-sensitive use cases. One could even make the case that Agents are the preferred user type for Web3 given their persistent uptime and capacity for broad data analysis and highly specialized decision-making. Unlike legacy financial systems, permissionless blockchains do not distinguish between humans and machines as transacting entities. They also provide a radically open data ecosystem to monitor and refine Agent behavior. Bringing Agent capabilities to Web3 would expand and enrich the native utility of decentralized infrastructure, unlocking use cases that require adaptive and context-aware behavior beyond what smart contracts alone can accommodate.
“Wallet-enabled agents can use any smart contract service or platform, from infrastructure services to DeFi protocols to social networks, which opens a whole universe of new capabilities and business models. An agent could pay for its own resources as needed, whether it’s computation or information. It could trade tokens on decentralized exchanges to access different services or leverage DeFi protocols to optimize its financial operations. It could vote in DAOs, or charge tokens for its functionality and trade information for money with other specialized agents. The result is a vast, complex economy of specialized AI agents talking to each other over decentralized messaging protocols and trading information onchain while covering the necessary costs. It’s impossible to do this in the traditional financial system.” — Joel Monegro, AI Belongs Onchain
Giza Agents: Future of Web3 Applications
The use cases of on-chain ML Agents are nothing short of a paradigm shift for Web3. However, the computation required to direct Agent behavior is too intensive to be handled directly on-chain. This integration requires a trust-minimized mechanism to interoperate high-performance computation with decentralized infrastructures. ZK-coprocessors have enabled this interoperability, providing significant scaling improvements.
By adopting this design pattern for bridging ML to Web3, Giza is enabling scalable integration of verified inferencing to on-chain applications. ML models are converted into ZK circuits, enabling their predictions to be integrated with on-chain applications conditional on proof verification. This allows for performant computation of ML models off-chain and trust-minimized execution of on-chain applications.
Think off-chain, act on-chain.
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