self evolving agentv1.1.0

A customer support agent uses the self-evolving loop to diagnose recurring issues, generate training units for common errors, and promote validated solutions to long-term behavior. This reduces escalation rates and improves response quality over time.

A coding agent uses the skill to track its code review mistakes, identify capability gaps (e.g., security vulnerabilities), and generate targeted practice units. It promotes only proven review strategies into its permanent guidelines, enhancing code quality.

A trading agent records losses and near-misses, diagnoses root causes (e.g., lagging indicators), trains on simulated data, and promotes only backtested strategies. This reduces risk and adapts to changing market conditions.

A medical AI uses the capability map to track its diagnostic accuracies across diseases, identifies weak areas (e.g., rare conditions), and generates training cases. It promotes only validated diagnostic rules after transfer tests on new patient data.

An autonomous driving agent logs edge-case errors (e.g., unusual pedestrian behavior), runs pre-task risk assessments, and trains on generated scenarios. Only strategies that generalize across environments are promoted, improving safety.

Offer the self-evolving agent as a cloud API where customers pay per learning cycle or per promotion event. This aligns cost with value, as users pay more when the agent actively improves.

Sell a perpetual license to enterprises for on-premise deployment, including custom configuration of capability maps and training units. Additional revenue from consulting and support.

Create a platform where users share and sell training units (e.g., industry-specific error scenarios). The agent skill integrates with this marketplace, taking a commission on each transaction.