Agentic Frameworks
Intent–Reasoning Synthesis
Tasmil Finance begins by interpreting the user’s intent from natural language input and synthesizing a strategic plan. The platform’s AI analyzes the user’s goals, matches them against a library of available decentralized finance (DeFi) agent strategies, and determines the optimal execution route to achieve the desired outcome. To enrich its reasoning, the system employs retrieval-augmented generation (RAG), drawing on up-to-date on-chain data (such as blockchain state, market prices, and protocol metrics) and off-chain information. This combined data context provides the agent with strategic insight, allowing it to formulate a well-informed plan aligned with the user’s objectives.
Isolated Validation
Before any real capital is executed, the proposed strategy undergoes isolated validation through an integrated large language model (LLM). In this stage, the LLM simulates the agent’s reasoning and potential outcomes in a safe, sandboxed environment, allowing the system to identify any logical errors or unfavorable results ahead of time. The simulation’s results - including expected actions and projected outcomes - are then presented to the user for approval. If the user chooses not to proceed, the framework archives the entire decision pathway (including the reasoning behind the strategy and the user’s response). This archived data serves as a learning example for the system, enabling it to refine future strategy recommendations based on past rejections or adjustments.
OrEx Agent (Orchestration + Execution)
Upon user approval, the framework transitions to the OrEx Agent stage, where the validated strategy is translated into a sequence of concrete on-chain actions and orchestrated for execution on the blockchain. The OrEx agent interacts directly with DeFi protocols to implement each step of the plan – for example, executing token swaps, providing liquidity, or initiating lending operations as required. All execution is handled autonomously by the agent, which encodes and dispatches transactions with the correct sequence and parameters.
Retrain Memory
In the final stage, all user decisions, strategy pathways, and transaction outcomes are logged into a dedicated memory layer, forming a historical record that is continuously used to retrain and fine-tune the platform’s AI models. By learning from actual outcomes and user feedback (both approvals and rejections), the system incrementally improves its future performance. The memory-driven feedback loop ensures that Tasmil Finance’s agentic framework is not static, but rather evolves with each interaction - enhancing strategic insight, safety, and alignment with user goals as the system gains experience.
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