Execution Logs and Common Errors
The Execution Logs provide an advanced, real-time breakdown of how an agent processes tasks, interacts with tools, and executes decisions. This section is intended for power users who want deeper insights into how the system operates under the hood. By examining execution logs, users can diagnose issues, optimize performance, and better understand how their agent handles tasks at a technical level.
Understanding Execution Logs
Each log entry represents a specific action taken by the agent, here are some examples:
Workflow Initiation – Logs the initial agent setup, including defined goals and strategies.
Node Execution – Displays when the agent activates specific processing nodes.
Tool Execution – Shows which tools (e.g., market analysis, social media posting, trade execution) were called and what data was retrieved or acted upon.
Completion Assessment – Evaluates whether a given step met its success criteria.
Performance Evaluation – Assesses how well an agent executed a goal, factoring in efficiency, tool utilization, and time metrics.
For example, a trading goal might show:
The agent fetching market data using a price inference tool.
The decision-making process for generating a trade order.
The execution of that trade on-chain.
A completion assessment to confirm whether the execution was successful.
Each action is timestamped, enabling users to trace execution flows and identify potential bottlenecks.
Common Errors and Troubleshooting
While Cod3x agents are designed for seamless execution, issues can arise due to missing data, incorrect tool initialization, or external service errors. Below are some common errors and their explanations:
Undefined or Null Property Errors
Cannot set properties of undefined (setting 'startTime')
Explanation: The agent is trying to reference a task ID that does not exist.
Solution: Ensure that the correct ID is being used for execution.
Cannot read properties of undefined (reading 'message')
Explanation: OpenAI’s API is failing or experiencing overload.
Solution: Retry the request or check OpenAI’s service status.
Cannot read properties of null (reading 'execution_steps')
Explanation: The system expected execution steps to be returned but received none.
Solution: Retry the goal, as the output may not have been properly generated.
Cannot read properties of null (reading 'efficiency_metrics')
Explanation: Similar to execution steps—failed to generate the expected output.
Solution: Adjust the goal structure and ensure all dependencies are initialized.
Tool Initialization Errors
toolNames.map is not a function
Explanation: The agent attempted to reference a tool that was not properly initialized or does not exist.
Solution: Verify that the tool name is correctly configured and available in the agent’s environment.
Using Execution Logs for Debugging
For advanced users, the execution logs serve as a real-time debugger, allowing you to:
Pinpoint where failures occur – By reviewing logs step by step, users can identify whether a failure happened at the goal initialization, tool execution, or completion assessment stage.
Optimize tool interactions – If certain tools are misfiring or returning incomplete data, logs can help determine if parameter adjustments are needed.
Monitor execution trends – Performance evaluations in logs provide insight into efficiency, tool utilization, and much more, helping users fine-tune their agent’s strategies.
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