Best Practices

To ensure your Cod3x agent performs effectively and improves over time, it's important to structure its setup, tasks, and feedback efficiently. While agents are designed to learn automatically, following best practices will help refine their decision-making and maximize their potential.

1. Set Clear Goals from the Start

Clearly define what you want your agent to accomplish, whether it's optimizing portfolio returns, maintaining a risk-balanced trading strategy, or increasing engagement on social media. Setting explicit long-term goals ensures the agent has a reference point for evaluating its performance and making adjustments over time.

2. Use Manual Training to Reinforce Desired Behaviors

Manual training allows you to fine-tune your agent’s actions. Adding instructions like "store this in memory" or "remember this strategy for future trades" helps guide the agent’s learning. Additionally, providing trade feedback or social engagement insights directly in the UI refines how the agent approaches similar tasks in the future.

3. Regularly Review Task Performance and Adjust Accordingly

Since Cod3x agents rely on goal tracking, data referencing, and run history, checking performance logs helps fine-tune strategies. If an agent's trades are consistently missing targets, reviewing past executions and adjusting risk settings can lead to better results. Similarly, social engagement patterns should be reviewed to optimize content strategies.

4. Leverage the Event-Based System for Smarter Automation

With Cod3x moving to a publish-subscribe (pub-sub) system, tasks and insights are handled dynamically. Agents will automatically subscribe to relevant updates, but users should still monitor how event triggers impact performance. Adjusting event-based subscriptions ensures the agent reacts to meaningful data instead of unnecessary noise.

5. Balance Automation with Oversight

While Cod3x agents are designed to operate independently, regular oversight ensures they stay aligned with evolving goals. Reviewing agent activity, refining scheduled tasks, and tweaking strategies based on feedback will help maintain optimal performance.

By following these best practices, users can ensure their Cod3x agents not only function autonomously but also continuously refine their decision-making processes, resulting in a smarter, more effective AI-driven assistant.