SELF-IMPROVEMENT

A new August 2026 paper argues that coding agents improve faster when they compare attempts across tasks and lineages, not just retry one failed trajectory.

Self-improving agents explained: reflection loops, Reflexion, LATS tree search, and meta agents, plus how to ground the critique step so the loop converges.

Harness engineering is where self-improving AI starts, Lilian Weng argues, not models rewriting their weights. What that means for developers building agents.

Self-improving AI agents that reflect on failures, store lessons and accumulate skills. Reflection loops, memory architectures and working code examples.