Agentic World

AI agentic world is growing, the intersection of AI and crypto could add a combined $20 trillion to the global economy by 2030. This will include contributions from various sectors such as automated trading, fraud detection, decentralized infrastructure, and more.

We choose the evolve the next paradigm for AGENTS & CRYPTO as we can observe the user demand and desire to have these services to the freely mobilize their time, money and efforts into something else meanwhile having huge set of specialized agents to perform tasks, thinking, and optimizing their techniques to produce desired output they want.

As blockchain technology provides a new template for transaction settlement, data storage, and system design. Artificial intelligence is a revolution in computation, analysis, and content delivery. Together it creates a bridge for extensive security for autonomous actions.

These agents are capable of receiving, interpreting, and executing tasks using an AI model which humans are not capable off.

Agents and crypto fit well together because of its permissionless and trustless payments infrastructure crypto provides. Once trained, agents can be given a wallet so they can transact with smart contracts on their own.

Agents are primed to become one of the largest consumers of decentralized compute and zkML , acting in an autonomous non-deterministic manner to receive and solve any task.

AT KOBOTO.AI we are trying to fill the missing gaps with fully autonomous agents will have the ability to figure out how to hire another agent to integrate the API and then execute the task. From the user perspective, there will be no need to check if an agent can fulfill a task because the agent can determine that themselves.

Though the current infra of AI getting interwoven with crypto has few bottlenecks and challenges that we had discussed on the infrastructure level such as COMPUTATIONAL OVERHEAD, AGENT MONETIZATION, GROUND SUBJECT EXPERT, ADVERSARIAL MACHINE LEARNING ATTACKS. But at the user layer its looks bit misaligned due to lack of usecases ,Innovation , Fear of loss through adoption & determinism from the infra as well ….

Many open-source AI projects operate through compute credits, revenue sharing, or other contractual arrangements with tech giants that grapple with the same structural dependencies but for developers on these open-source platforms there is no way to monetize as these model weights can copied and packed in the black box and be monetized by them

>With our incentive engineering, now agents builders can monetize their agents in a fair & distributable manner. While having a modular & dynamic environment to produce inference from interactive and aggregated foundation.

Someone builds eg. a prediction market or a stablecoin that uses an AI oracle, and it turns out that the oracle is attackable, that’s a huge amount of money that could disappear in an instant.

> Prediction markets have not taken off too much in practice, and there is a series of commonly given reasons why: the largest participants are often irrational, people with the right knowledge are not willing to take the time and bet unless a lot of money is involved, markets are often thin, etc.

Take a general case of prediction market where due to less liquidity it won’t drive user drive over the app, but imagine a paradigm where agents are predicting against each other. The incentive to do a good job on any one question may be tiny, the incentive to produce AI agents that makes good predictions in general is in the millions.

Take the case of Worldcoin for instance — The main defense that Worldcoin is relying on is the fact that it’s not letting anyone simply call into the AI model: rather, it’s using trusted hardware to ensure that the model only accepts inputs digitally signed by the orb’s camera.

This approach is not guaranteed to work: it turns out that you can make adversarial attacks against biometric AI that come in the form of physical patches or jewelry that you can put on your face ….

>Hiding the AI model itself, greatly limiting the number of queries, and requiring each query to somehow be authenticated, you can make adversarial attacks difficult enough that the system could be secure. In the case of Worldcoin, increasing these other defences could also reduce their dependence on trusted hardware, increasing the project’s decentralization .

THESE ARE THE FEW MISSING GAPS AND ISSUES WITH CURRENT TECHNOLOGIES BUILDING AT THE INTERSECTION CRYPTO|AI and we are solving these problems in coordination , openness and by bring nash equilibrium to the forsteps of incentive design, meanwhile advocating new paradigms for models to create agent economy.

We at koboto solving it with modular agent economy leveraging models build blockchain datasets , verifiable inference choices ( ZK, Optimistic and Trusted execution environment ) ; wider usecases which address the automation and security of crypto economy .

A future where koboto is going to integrate with EZKL for proof generation and will likely soon add functionality from others leading providers. Nodes on KOBOTO could also use Akash or io.net GPUs and query models trained on Bittensor subnet . And if some agent is not available on our network we can build using Gensyn submitters. This is only possible dynamic and modular design of koboto network.

At koboto , our mission is provide a dynamic & modular stack for agent builders to run their node in order to provide flexible, fast and verifiable inference that is been desired by the inference consumer.

Koboto network paving the way for machine intelligence to become fully commoditized and integrated with the economy, technology, and society.