self-challenge

Automate continuous self-improvement cycles by implementing a dual-agent architecture where a Curriculum agent designs domain-specific challenges and an Executor agent attempts them, logging outcomes for progressive capability growth.

A dual-agent challenge loop is a self-evolution mechanism where one agent designs domain-specific tasks and another executes them, enabling structured capability expansion and knowledge updates across iterative cycles.

Track learning outcomes and skill upgrades by maintaining a challenge-log and updating a memory file (MEMORY.md), which records task results, learned skills, and knowledge updates for future review cycles.

Yes, this self-challenge workflow requires a dual-agent architecture consisting of a Curriculum agent to design challenges and an Executor agent to attempt them, alongside memory and challenge-log updates.

Yes, you can use this approach for tool mastery and problem-solving by applying ready-to-use templates that schedule weekly challenges, enabling real-world continuous improvement in domain-specific automation tasks.

Structure phase-driven learning by separating the workflow into Review & Learn and Knowledge Update phases, ensuring the agent evaluates challenge outcomes before updating its memory and learned-skills registry.