Autonomy – Self-Harness/Self-Directed AI Agent Core Under Development

What it does

AgentLoop.run(goal, max_steps=12) 啟動後完全自主執行，每步驟透過 run_turn() 推進。支援互動模式（ interactive=True ）與批次模式，所有事件均以 run_id 記錄於 AutonomyStore 。 CandidateSelector 以 evidence_strength（+0.30）、purpose（+0.10）、risk（−0.35）、side_effects（−0.20）、penalty（−1.0）五個維度計算總分，選出前 beam_width=3 名候選路徑送往執行。 每次 Run 結束後， LearningLoop.review_run() 自動觸發：若結果為 ACHIEVED 且有 ≥2 個成功步驟，自動起草新 ProcedureSkillDraft （信心度 0.85），生成 LearningProposal 等待審核。 每次 Run 結束後， CuratorDaemon 在後台執行緒（ daemon=True ）非同步啟動 SkillCurator.apply_auto_merges() ，自動偵測並合併重複（信心 0.95）或子集（信心 0.90）技能，防止技能庫膨脹。 mcp 工具集透過 mcp_servers.yaml 匯入外部 Model Context Protocol 工具。每個工具以 mcp_<server>_<tool> 格式經由 ToolRegistry 與 ActionGateway 受管執行，風險等級 MEDIUM。 memory 工具集（預設啟用）提供…from autonomy-landing-page.vercel.app

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