Subtasks
Cod3x agents break down tasks into steps and subtasks, providing transparency into how each action is performed. Subtasks serve as a detailed look at what happens within a given step, offering insights into which tool was used, what parameters were applied, the reasoning behind the action, and the data retrieved or executed.
This structured execution allows users to track exactly how decisions are made, what information is processed, and how outcomes are determined—whether it’s trading, market analysis, or social media interactions.
Understanding Subtasks
Subtasks are not separate executions but rather a deeper window into how a specific step within a run was carried out. They provide a detailed breakdown of:
Tool Execution – Identifies the specific plugin or function the agent used.
Parameter Inputs – Details the variables that shaped the tool’s output.
Reasoning – Explains why the agent made a particular decision.
Data Retrieved – Shows the results pulled from external sources or generated through internal calculations.
For example, if an agent is executing a market analysis task, it might retrieve key indicators such as price trends, RSI values, MACD crossovers, and Bollinger Bands. Instead of simply providing the final decision, the subtask reveals the full breakdown of why that decision was made.
In some cases, the subtask will display technical analysis results in a structured format, detailing recent price movements, overbought/oversold conditions, and potential trading signals. This provides a clear rationale for trade execution, helping users understand the logic behind their agent’s trading strategy.
Breaking Down Analysis and Decision-Making
Some tasks require extensive data processing, and subtasks will log every relevant detail to ensure full transparency. For instance, when an agent analyzes market conditions, it may outline:
Recent price movements and closing prices.
Momentum indicators like RSI and MACD.
Support and resistance levels using Bollinger Bands.
Key observations, such as whether an asset is in an uptrend or facing bearish pressure.
These details allow users to audit the agent’s thought process and confirm that decisions align with their intended strategy.
Similarly, when an agent executes a trade, the subtask will show:
The chosen order type (market or limit).
The selected asset pair and trade size.
The reasoning behind risk management settings, such as stop-loss and take-profit levels.
By presenting all this information within the Goal History, users gain a complete picture of why and how trades are executed.
Execution-Based Subtasks
Not all subtasks involve analysis—some focus on direct execution. For instance, if an agent places a trade, the subtask will log:
The selected token and exchange route.
The trade size and risk parameters.
The final decision to buy, sell, or hold.
The most important part of this process is the reasoning, which explains why a specific action was taken based on the agent’s evaluation of market conditions. Users can reference this reasoning to validate their agent’s behavior and fine-tune its decision-making if necessary.
Similarly, in social-based tasks, subtasks can capture interactions such as retrieving trending topics or analyzing sentiment on X (Twitter). The breakdown will show:
Which accounts or hashtags were monitored.
The engagement level of retrieved tweets.
The sentiment score of certain discussions.
This allows users to see exactly how their agent is identifying trends and formulating content strategies.
Enhancing User Control and Debugging
The subtask system serves two critical functions:
It provides full transparency into how agents operate, ensuring users can trust the decisions being made.
It allows for easy debugging and optimization, helping users adjust parameters or refine strategies if an agent isn’t performing as expected.
At the bottom of the Goal History, users can also make quick edits to adjust an agent’s:
Personality and decision-making style.
Social engagement approach.
Trading strategy and execution preferences.
Goal settings for improved precision.
By regularly reviewing subtasks and fine-tuning strategies, users can ensure that their agents operate efficiently, adapt to market conditions, and align with their intended objectives.
Last updated