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