propose-harness-improvement

To systematically analyze agent failures, you diagnose execution errors, classify root causes by context or architecture, and document fixes to prevent recurring errors. This structured process logs failure details directly into your system documentation.

Documenting recurring agent execution errors is achieved by logging failure details and their corresponding fixes into a dedicated markdown file. This creates a permanent record in your system documentation for future troubleshooting and fault prevention.

Classifying root causes of agent misbehavior involves determining whether failures stem from missing context, broken rules, inadequate skills, flawed architecture, or poor prompt design. This categorization targets the exact source for effective fault prevention.

Yes, you can triage agent failures without external dependencies because the process operates independently using built-in scripts and references. It guides you through diagnosing execution errors and generating structured reports natively.

When an AI agent consistently fails in specific scenarios, tracing the issue requires structured problem analysis to identify missing context or prompt design flaws. Documenting these findings improves overall system robustness and prevents future recurrences.

Manual debugging for agent failure analysis lacks systematic categorization and automated process documentation, making it harder to prevent future errors. A structured approach ensures failures are consistently classified and logged for robust fault prevention.