Agents as applications
Agent builders can build around these use cases or they can build their own systems by filling the missing gap by which is currently in need by inference consumer
Optimal Asset Allocator
The core objective of any token distribution abstraction within DeFi protocols, be it vaults, aggregators, or other mechanisms, is to adeptly solve for the asset allocation problem.
Given the context, the allocation process could maximize APY for lenders, increase the total borrowed in subsequent timeframes, or minimize risk. These architectures are built on a framework that ensures transparency into the operational formulas — such as interest rates and APY — enables the strategic re-allocation of assets among various pools or silos and provides clear parameters for each underlying entity.
Silo : Taking this name from the Frax protocol, a Silo is defined as a decentralized and permissionless lending protocol that allows for the creation of risk-isolated markets. Each silo has a parameterizable interest function, stores the state of the number of available and borrowed tokens, the type of accepted token, the maximum number of tokens allowed, and offers functions for rapid state updates. It can provide any information related to its current state.
Vaults: A vault, understood as an object in charge of performing allocations, has information about all silos simultaneously, offers functionality such as knowing the total APY obtained after an allocation, the list of available silos, or the total liquidity available across all silos.
Optimizers: Each optimizer must decide, given an event in the protocol, what allocation within the constraints of the problem is optimal.
Events: What can a user do in the protocol? Examples of events can be; lend, borrow or create custom silos.
AI-Powered APY% maximizer
Current DeFi return strategies often struggle with real-time market responsiveness and are limited in their ability to process vast datasets, resulting in less optimized returns. AI, on the other hand, can analyze data with speed and efficiency that no human could replicate, overcoming these limitations.
Based on these analyses, DeFi protocols could implement an AI model that develops dynamic return strategies, involving a combination of buying, holding, and selling at specific intervals or price points. This strategy would then be executed automatically to optimize for maximum return, ensuring anyone has access to institutional-grade investing strategies.
Enhanced Prediction Agents
Traditional prediction markets often suffer from low liquidity, especially in niche areas. AI agents can overcome these limitations by efficiently managing market making and trading strategies. AI agents, being more efficient than human traders, can significantly increase trading volumes. This higher activity boosts liquidity, making markets more efficient, all while delivering more accurate projections across niche events.
Prediction markets could submit their model on koboto Network that specialize in various fields, analyzing vast datasets including historical trends, current events, and nuanced market indicators, and then trade in different prediction markets based on those analyses. This leads to more granular predictions that were previously unattainable. These agents could then manage on-chain DeFi vaults, using deposited funds to trade across relevant prediction markets autonomously, thus facilitating dynamic market making and trade execution to bolster liquidity and volume.
Optimized Liquidity Management Agents
Perp DEXs rely on active and granular liquidity management to function effectively, which has historically led to inefficiencies, such as slippage, delayed responses to rapid market movements, and difficulty in maintaining optimal order book depth.
AI can analyze market conditions in real-time, predicting periods of high volatility or trading volume. Based on these predictions, the AI can dynamically adjust the liquidity in the order book. This means maintaining a healthy ratio of buy and sell orders, which is crucial for market stability, thereby safeguarding value for traders by minimizing slippage from sudden market movements.
Market makers on platforms could further elevate their offerings by integrating AI-powered price feeds for liquidity optimization. By predicting periods of peak volatility, liquidity providers can preemptively adjust liquidity parameters across their decentralized order books, upholding balanced order ratios aligned with upcoming conditions — essential for platform equilibrium and minimal slippage.
Maximal Extractable Value (MEV0) Agent
MEV0 will transform how transactions are optimized on-chain. MEV0 is a agent subnet for providing multiple mev services on koboto.ai.
AI could be used to predict future actions of crypto investors, which would enable the execution of advanced MEV strategies that go beyond current simulation-based methods, including transaction reordering, insertion, and the creation of new transactions. By harnessing probabilistic and multi-block profit-extracting strategies, MEV0 bots could identify and exploit profitable opportunities with unprecedented precision and efficiency.
For block builders and searchers, AI agents could directly execute or recommend specific actions to capitalize on arbitrage opportunities by reordering or creating transactions. This precise, real-time strategy enhancement could significantly boost the efficiency and profitability of trades on these DEXs.
And by proving MEV0 services to the application in order to extract from their application and inclined there MEV towards their dAPP user.
DAO Governance Agents
AI agents could solve the widespread issue of voter apathy in DAOs. These agents would introduce a means for precise, data-driven participation, thereby combating voter apathy and the frequent misalignment between DAO delegates and their constituent interests. The absence of such technology has previously left DAOs and political systems alike struggling to maintain active and representative governance structures.
AI agents could participate in governance decisions, automatically voting on proposals or aiding in the formulation of policies. These AI representatives would be trained on the individual political preferences and values of their constituents, enabling them to automatically vote in alignment with these interests. These agents could potentially represent the specific preferences of their constituents more accurately than human representatives. This automated involvement would ensure all decisions are consistently aligned with the community’s interests. Ultimately, this would foster a governance environment that is more engaged and in tune with the collective will.
Decentralized Credit Scoring
Traditional credit scoring systems often fail to capture the full picture of an individual’s financial health, relying on opaque criteria that can introduce biases. Decentralized AI offers a pathway to more accurately assess creditworthiness by analysing a wide array of financial behaviours directly onchain, resulting in a more nuanced and fair evaluation of financial health.
For instance, when a user applies for a loan on a DeFi platform, the platform’s AI can analyse their transaction history, including payment patterns and asset diversity, to assign a dynamic credit score. This score, grounded in transparent and verifiable data analysis, would then be used to determine the user’s loan eligibility and terms, ensuring fair and unbiased results. The continuous update of this score, based on evolving financial behaviors, would also ensure an up-to-date reflection of the user’s credit status. Decentralized credit scoring could democratize access to financial services, ensuring that individuals are assessed based on their actual financial practices rather than a narrow set of traditional criteria.
Sentiment Analysis Agent for Trading
Traditional market analytics struggle to capture shifting market sentiments in real-time. Using AI to determine market sentiment would allow traders to leverage a multitude of data inputs, providing a nuanced view of market attitudes toward specific assets or the broader financial landscape. Previously, the sheer volume and variety of data required for effective sentiment analysis made real-time insights difficult.
For instance, traders could utilize AI to monitor and analyse sentiment on social media and financial news platforms, capturing the collective mood regarding cryptocurrencies or stocks. This AI-driven insight would allow traders to adapt their strategies immediately, perhaps even triggering autonomous AI agents to trade on their behalf based on this sentiment analysis.
Portfolio Tracker Agent
Current crypto portfolio trackers only provide a superficial view of holdings without in-depth risk analysis or strategic advice. An AI model could assess a specific portfolio’s risk based on its performance against market benchmarks, and even provide strategic insight for portfolio optimization or even copy-trading.
For example, by analysing a portfolio’s historical performance data, current market trends, and individual asset volatility, AI can suggest strategies for rebalancing, diversification, or exploring new investment opportunities tailored to the user’s risk tolerance and goals. Beyond mere tracking, this AI model could allow users to emulate the strategies of successful portfolios, thereby democratizing access to expert investment tactics.
And there will be more agent &networks bandwidth implementation will be adding in our roadmap in the future…
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