Agentic Design Patterns

INFINIT agents collaborate using four key thinking frameworks that enable sophisticated coordination and continuous improvement across the entire ecosystem:

Multi-Agent Collaboration

Multi-Agent Collaboration enables specialized agents to share data and coordinate actions seamlessly through structured communication protocols. When executing complex cross-protocol strategies, agents establish execution dependencies and resource sharing agreements.

The collaboration architecture works through:

• Each agent maintains domain expertise (lending, trading, bridging, etc.)

• Dynamic agent selection based on strategy requirements

• Real-time contextual data sharing between agents

• Coordinated timing for multi-step transaction sequences

This ensures that a leveraged yield farming strategy requiring Aave lending/borrowing, Pendle swapping PT Tokens, and Hyperliquid depositing and opening a short/long position executes as a single coordinated operation rather than three independent transactions.

These patterns form the operational intelligence that transforms individual specialized agents into a cohesive execution network.

Self-Reflection

Self-Reflection allows agents to continuously review their performance and utilize feedback loops to improve accuracy over time. Each agent maintains execution history, outcome tracking, and performance metrics that feed back into decision-making algorithms.

The self-reflection cycle includes:

• External data validation against current market conditions

• User feedback integration for accuracy improvements

• Predictive guidance based on interaction history

• Saved prompts for recurring user workflows

• Adaptive intelligence that evolves with user behavior

Agentic RAG

Agentic RAG enables agents to pull clean on-chain and off-chain data from INFINIT Data Stream for precise, context-aware decisions to complete complex tasks with the latest and relevant information.

The agentic RAG capabilities include:

• Real-time protocol state retrieval

• Social sources and expert opinion integration

• Opportunity prioritization through multi-source validation

• Cross-chain liquidity and opportunity assessment

• User-specific portfolio analysis

This approach allows agents to include current market states as contextual factors in recommendations in addition to user context, ensuring strategy suggestions reflect real-time protocol conditions, liquidity availability, and yield opportunities.

ReAct Agent

ReAct Reasoning provides step-by-step reasoning and action coordination for personalized DeFi execution through automated sequential processing. Agents decompose any strategy, whether requiring 10 steps for simple yield farming or 50 steps for complex multi-protocol leverage operations, into granular execution sequences that they reason through systematically.

The framework operates through iterative reasoning cycles where agents evaluate each step's prerequisites, execute the action, then reason about the next step based on actual outcomes.

Despite the automated reasoning, users are rest assured that they make decisions at critical strategy points while agents handle all technical execution steps.