Agentic Context Engineering (Evolving Playbook)(ACE)

Treats an agent's context as an evolving playbook of concrete strategies that is improved by three roles: a Generator that produces reasoning traces on real tasks, a Reflector that extracts lessons from what succeeded or failed, and a Curator that merges those lessons into the playbook as compact structured delta updates. Because updates are incremental appends and edits rather than full rewrites, the playbook accumulates domain detail instead of suffering context collapse and brevity bias, where iterative rewriting erodes hard-won specifics. It needs no labeled supervision and applies both offline as an improved system prompt and online as agent memory, with the ICLR 2026 ACE paper reporting roughly +10.6% on agent tasks and +8.6% on finance. Distinct from `prompt-optimization`: DSPy and GEPA optimize a prompt artifact against a metric, whereas ACE grows a natural-language playbook through delta curation with an explicit anti-collapse mechanism spanning prompt and memory, and unlike `skill-library` (executable skills) or `self-improving-systems` (governed prompt and tool edits behind approval gates) the improving artifact is a curated natural-language playbook.