Agent Framework

Deep dive into our DAG-based workflow system and how it enables sophisticated trading strategies.

The Agent Framework represents the intelligence layer of Replicats, orchestrating complex trading strategies through a sophisticated DAG-based workflow engine.

DAG-Based Orchestration

• Directed Acyclic Graph (DAG) Foundation Our agent framework uses a DAG to coordinate tasks such as data ingestion, model inference, risk checks, and trade execution.

  • Each node in the DAG represents a specialized step (e.g., reading time series data, querying the knowledge graph, or calling a predictive model).

  • The edges define how data flows from one step to another without circular dependencies.

• Conditional & Event-Driven Logic

  • Agents can branch into different paths based on probabilistic triggers (e.g., a predicted 80% chance of price rally) or deterministic thresholds (e.g., price surpasses $30,000).

  • This flexibility allows for complex strategies, from simple rebalancing rules to multi-step yield farming sequences.

Multi-Model Integration

• Specialized AI Models for Triggers

• Targeted LLM Usage

  • While specialized models handle the bulk of predictions, we still leverage LLMs (via DSPy) for natural language queries, high-level summaries, or user interactions.

  • The DAG calls the LLM steps only where human-like reasoning adds value—keeping cost and latency under control.