- 4.1 Fallback chain: LLM fallback_llm config in AgentLLMConfig, retry with alternate model on API failure; Agent fallback_agent in DAG nodes
- 4.2 Knowledge sharing: GlobalKnowledge model with embedding-based semantic search, auto-extraction of tool names as tags after execution
- 4.3 Async execution: execute_agent_task fully implemented with AgentRuntime, scheduler dual-path for workflow/non-workflow agents
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2.1 Orchestrator in workflow:
- New run_orchestrator_node() in workflow_integration.py loads agents from DB,
supports route/sequential/debate/pipeline modes
- New 'orchestrator' node type in workflow_engine.py execute_node dispatch
2.2 Tool-level human approval:
- AgentToolConfig extended with require_approval, approval_timeout_ms,
approval_default fields
- New ApprovalManager (approval_manager.py) with asyncio.Event-based
create/wait_for_decision/resolve pattern
- AgentRuntime run() and run_stream() intercept tool execution,
wait for approval decision before executing
- New POST /api/v1/approval/{id}/resolve REST endpoint
- Frontend: approval_required SSE event handling, approval dialog UI
with approve/deny/skip buttons
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Add enterprise_review tool (35th builtin) for LLM-based quality assessment
- Add evaluator workflow node type for quality gating in DAG
- Add AgentRuntime built-in self-review with auto-correction loop
- Rewrite error_handler node from stub to real retry mechanism
- Add engine-level per-node retry with configurable max_retries/delay/on_exhausted
- Add AgentExtension model for extension tracking
- Enhance validation in agent_create_tool and tool_register_tool
- Update 全能助手 system prompt with self-evolution workflow
- Docs: 缺失能力.md and 解决缺失能力计划.md
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
