Self-Improving Agent (With Self-Reflection)

To make an AI agent learn from corrections, you need a self-reflection loop that logs user feedback and updates behavior. This skill permanently improves agent performance by keeping a corrections-driven memory and promoting repeatable improvements across tasks.

The best way to implement self-improvement for automation tasks is using domain-aware learning that applies feedback across global and domain contexts. This skill evaluates outputs against user corrections, enabling long-term memory and pattern promotion for coding, writing, or planning.

A tiered memory system for AI agent self-reflection works by categorizing memory into HOT, WARM, and COLD tiers with clear logging and promotion rules. This enforces privacy boundaries and local storage while guiding behavior through long-term memory retention.

Yes, you need local storage and memory files to set up a self-improving agent. The system enforces privacy boundaries and requires memory files to maintain a tiered HOT, WARM, and COLD system that logs corrections and promotes behavioral improvements.

Yes, you can apply project-specific overrides to an AI agent's long-term memory. The self-improving agent supports domain-aware learning, allowing you to provide project-specific overrides that guide behavior alongside global and domain context improvements.

Use a corrections-driven memory approach to permanently improve agent performance over standard automation. By evaluating outputs against feedback and promoting repeatable improvements, the agent adapts its behavior across iterative tasks rather than repeating the same errors.