Agents produce structured outputs that Systemiq stores as records. These outputs might be:

• Insights (e.g., “High-priority bug spike detected”)
• Summaries (e.g., “Average user satisfaction improved by 10%”)
• Numerical / float values Quantitative metrics used in trend analysis and timeseries data (e.g., “Error rate: 3.5%” or “Estimated workload increase over next week: 15%”).
• Categorical values Represent distinct states or classifications, such as alerts or process statuses (e.g., “Alert level: High,” “Process status: Pending”).
• Predictions or recommended actions (e.g., “System overload likely in 3 days – allocate additional resources”)

AI agents generate predictive insights and summaries based on data patterns, while logic agents follow predefined rules to trigger alerts or transformations. These outputs enable real-time decision-making and continuous process optimization.