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497 lines
18 KiB
JSON
497 lines
18 KiB
JSON
{
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||
"blocked_gates": [
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"sdk_installation_approved=false",
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"paid_api_calls_approved=false",
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"production_routing_approved=false",
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"telegram_send_approved=false",
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"model_provider_switch_approved=false",
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"host_write_approved=false",
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"openclaw_replacement_approved=false",
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"replay_shadow_canary_gate_required=true",
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"cost_and_data_boundary_review_required=true"
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],
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"generated_at": "2026-06-25T03:56:51.751955+00:00",
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"high_priority_review_queue": [],
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"integration_candidates": [
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{
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"awoooi_role": "協調者、handoff、tool tracing、guardrail 候選",
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"changed": false,
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"decision": "watch_only_no_change",
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"display_name": "OpenAI Agents SDK",
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"integration_surface": "agent_handoff_tracing_guardrails",
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"recommended_actions": [
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"keep_watch_only_status"
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],
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"technology_area": "agent_frameworks",
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"technology_id": "openai_agents_sdk"
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},
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{
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"awoooi_role": "NemoTron replay / evaluator / synthetic data gate",
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"changed": false,
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"decision": "watch_only_no_change",
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"display_name": "NVIDIA Nemotron + NeMo Agent Toolkit",
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"integration_surface": "offline_replay_evaluator_smoke_gate",
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"recommended_actions": [
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"keep_watch_only_status"
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],
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"technology_area": "agent_frameworks",
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"technology_id": "nvidia_nemotron_nemo"
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},
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{
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"awoooi_role": "事件處理與可恢復 workflow kernel 候選",
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"changed": false,
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"decision": "watch_only_no_change",
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"display_name": "LangGraph",
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"integration_surface": "durable_workflow_human_in_loop",
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"recommended_actions": [
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"keep_watch_only_status"
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],
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"technology_area": "agent_frameworks",
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"technology_id": "langgraph_runtime"
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},
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{
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"awoooi_role": "Gemini/Vertex agent stack watch-only 候選",
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"changed": false,
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"decision": "watch_only_no_change",
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"display_name": "Google Agent Development Kit",
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"integration_surface": "gemini_enterprise_agent_stack",
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"recommended_actions": [
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"keep_watch_only_status"
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],
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"technology_area": "agent_frameworks",
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"technology_id": "google_adk_stack"
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},
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{
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"awoooi_role": "MCP/A2A enterprise workflow watch-only 候選",
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"changed": false,
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"decision": "watch_only_no_change",
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"display_name": "Microsoft Agent Framework",
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"integration_surface": "enterprise_mcp_a2a_workflow",
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"recommended_actions": [
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"keep_watch_only_status"
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],
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"technology_area": "agent_frameworks",
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"technology_id": "microsoft_agent_framework"
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},
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{
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"awoooi_role": "快速 prototype / non-production 評估候選",
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"changed": false,
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"decision": "watch_only_no_change",
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"display_name": "CrewAI Flows + Crews",
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"integration_surface": "multi_agent_prototype",
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"recommended_actions": [
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"keep_watch_only_status"
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],
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"technology_area": "agent_frameworks",
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"technology_id": "crewai_flows"
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},
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{
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"awoooi_role": "read-only tool registry / MCP adapter 候選",
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"changed": false,
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"decision": "watch_only_no_change",
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"display_name": "Model Context Protocol SDK",
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"integration_surface": "tool_registry_interoperability",
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"recommended_actions": [
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"keep_watch_only_status"
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],
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"technology_area": "mcp_and_a2a",
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"technology_id": "modelcontextprotocol_sdk"
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},
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{
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"awoooi_role": "跨 Agent 溝通協定 watch-only 候選",
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"changed": false,
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"decision": "watch_only_no_change",
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"display_name": "Agent2Agent Protocol",
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"integration_surface": "agent_to_agent_interop",
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"recommended_actions": [
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"keep_watch_only_status"
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],
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"technology_area": "mcp_and_a2a",
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"technology_id": "a2a_protocol"
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},
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{
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"awoooi_role": "模型能力、成本與 routing scorecard 來源",
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"changed": false,
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"decision": "watch_only_no_change",
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"display_name": "OpenAI Model Platform",
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"integration_surface": "model_capability_cost_routing",
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"recommended_actions": [
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"keep_watch_only_status"
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],
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"technology_area": "model_providers",
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"technology_id": "openai_model_platform"
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},
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{
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"awoooi_role": "Claude model / coding agent / remediation watch source",
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"changed": false,
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"decision": "watch_only_no_change",
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"display_name": "Anthropic Claude Platform",
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"integration_surface": "model_capability_cost_routing",
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"recommended_actions": [
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"keep_watch_only_status"
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],
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"technology_area": "model_providers",
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"technology_id": "anthropic_claude_platform"
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},
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{
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"awoooi_role": "Gemini model capability / cost watch source",
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"changed": false,
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"decision": "watch_only_no_change",
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"display_name": "Google Gemini Platform",
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"integration_surface": "model_capability_cost_routing",
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"recommended_actions": [
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"keep_watch_only_status"
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],
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"technology_area": "model_providers",
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"technology_id": "google_gemini_platform"
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},
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{
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"awoooi_role": "RAG ingestion / indexing / connector watch source",
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"changed": false,
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"decision": "watch_only_no_change",
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"display_name": "LlamaIndex",
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"integration_surface": "rag_indexing_connectors",
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"recommended_actions": [
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"keep_watch_only_status"
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],
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"technology_area": "rag_and_vector",
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"technology_id": "llamaindex_rag"
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},
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{
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"awoooi_role": "LLM app integration connector watch source",
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"changed": false,
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"decision": "watch_only_no_change",
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"display_name": "LangChain",
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"integration_surface": "llm_app_runtime_connectors",
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"recommended_actions": [
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"keep_watch_only_status"
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],
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"technology_area": "rag_and_vector",
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"technology_id": "langchain_runtime"
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},
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{
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"awoooi_role": "現有 Postgres/pgvector 能力與版本 freshness 來源",
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"changed": false,
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"decision": "watch_only_no_change",
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"display_name": "pgvector",
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"integration_surface": "postgres_vector_index",
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"recommended_actions": [
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"keep_watch_only_status"
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],
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"technology_area": "rag_and_vector",
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"technology_id": "pgvector_vector_store"
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},
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{
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"awoooi_role": "專用 vector DB 候選，只能 sandbox 評估",
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"changed": false,
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"decision": "watch_only_no_change",
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"display_name": "Qdrant",
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"integration_surface": "dedicated_vector_database",
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"recommended_actions": [
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"keep_watch_only_status"
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],
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"technology_area": "rag_and_vector",
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"technology_id": "qdrant_vector_store"
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},
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{
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"awoooi_role": "本機 / sandbox vector store 候選",
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"changed": false,
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"decision": "watch_only_no_change",
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"display_name": "ChromaDB",
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"integration_surface": "local_vector_database",
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"recommended_actions": [
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"keep_watch_only_status"
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],
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"technology_area": "rag_and_vector",
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"technology_id": "chromadb_vector_store"
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},
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{
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"awoooi_role": "RAG / LLM app evaluation metrics 候選",
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"changed": false,
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"decision": "watch_only_no_change",
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"display_name": "Ragas",
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"integration_surface": "rag_eval_metrics",
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"recommended_actions": [
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"keep_watch_only_status"
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],
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"technology_area": "evaluation_and_observability",
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"technology_id": "ragas_eval"
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},
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{
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"awoooi_role": "LLM trace / prompt / eval observability 候選",
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"changed": false,
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"decision": "watch_only_no_change",
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"display_name": "Langfuse",
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"integration_surface": "llm_observability_tracing",
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"recommended_actions": [
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"keep_watch_only_status"
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],
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"technology_area": "evaluation_and_observability",
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"technology_id": "langfuse_observability"
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},
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{
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"awoooi_role": "自託管模型 serving 能力與版本 freshness 來源",
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"changed": false,
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"decision": "watch_only_no_change",
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"display_name": "Hugging Face Text Generation Inference",
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"integration_surface": "self_hosted_model_serving",
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"recommended_actions": [
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"keep_watch_only_status"
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],
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"technology_area": "model_serving",
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"technology_id": "huggingface_tgi"
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},
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{
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"awoooi_role": "自託管 LLM inference 候選，需 GPU/成本/安全 gate",
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"changed": false,
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"decision": "watch_only_no_change",
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"display_name": "vLLM",
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"integration_surface": "self_hosted_llm_inference",
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"recommended_actions": [
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"keep_watch_only_status"
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],
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"technology_area": "model_serving",
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"technology_id": "vllm_serving"
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}
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],
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"policy": {
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"host_write_approved": false,
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"model_provider_switch_approved": false,
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"openclaw_replacement_approved": false,
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"paid_api_calls_approved": false,
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"production_routing_approved": false,
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"raw_chat_history_synced": false,
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"read_only": true,
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"sdk_installation_approved": false,
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"telegram_send_approved": false
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},
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"priority_workplan": [
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{
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"automation_mode": "agent_auto_read_only",
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"done_definition": "API、snapshot、Markdown、schema、測試與 production readback 都能顯示技術領域、來源與 Gate。",
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"order": 1,
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"priority": "P0",
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"work_item": "AI 技術雷達 primary source 監控產品化"
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},
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{
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"automation_mode": "agent_auto_schedule_read_only",
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"done_definition": "每 6 小時可跑 watch；失敗來源會進日報，不會自動整合。",
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"order": 2,
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"priority": "P0",
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"work_item": "近即時版本 / release / docs 變更偵測"
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},
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{
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"automation_mode": "agent_auto_read_model_human_review_for_write",
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"done_definition": "每個 Agent 的角色、輸出、學習寫回與限制都能被前端讀回。",
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"order": 3,
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"priority": "P0",
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"work_item": "OpenClaw / Hermes / NemoTron / MarketRadar 專業分工與成長紀錄"
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},
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{
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"automation_mode": "agent_propose_owner_review",
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"done_definition": "高優先級變更先進 scorecard，再進 no-cost/no-write sandbox 或 replay 計畫。",
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"order": 4,
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"priority": "P1",
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"work_item": "AI 技術 scorecard 與 sandbox / replay 優先級"
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},
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{
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"automation_mode": "blocked_until_telegram_send_gate",
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"done_definition": "低中風險只告警回報；高風險需 owner approval 後才可發送或執行。",
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"order": 5,
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"priority": "P1",
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"work_item": "Telegram Bot 報告與高風險審核橋接"
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},
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{
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"automation_mode": "agent_auto_discover_human_classify",
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"done_definition": "GitHub topic / package registry / 官方 blog 可提出候選，但加入正式 watchlist 前需審核。",
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"order": 6,
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"priority": "P2",
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"work_item": "新 AI 技術探索與 watchlist 擴充"
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}
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],
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"professional_agent_roles": [
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{
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"agent": "OpenClaw",
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"auto_scope": "維持現有 production baseline、讀取 replay / shadow 評分、拒絕無證據替換",
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"professional_role": "生產決策仲裁者、風險分級與最後 policy guard",
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"review_boundary": "任何取代、降級、生產路由切換都必須通過 replay / shadow / canary 與人工批准。"
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},
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{
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"agent": "NemoTron",
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"auto_scope": "只讀 request pack、比對候選輸出、產生 replay scorecard 草稿",
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"professional_role": "離線回放評估者、模型能力比較、合約輸出 smoke gate",
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"review_boundary": "不得自行呼叫外部 NIM/API、不得讀 labels 作答、不得進生產路由。"
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},
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{
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"agent": "Hermes",
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"auto_scope": "整理 primary source 摘要、建立 no-send 日週月報、準備人審包",
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"professional_role": "知識管理、RAG 整理、報告草稿與長期技能庫維護",
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"review_boundary": "不得同步 raw chat history、不得保存 secret、不得直接發 Telegram live report。"
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},
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{
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"agent": "MarketRadar",
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"auto_scope": "每 6 小時只讀 primary sources、產生 freshness / review queue",
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"professional_role": "AI 技術市場雷達、版本監控、來源失敗偵測",
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"review_boundary": "不得自動新增 SDK、不得自動修改 provider route 或 workflow 行為。"
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},
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{
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"agent": "Critic / Reviewer",
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"auto_scope": "檢查政策旗標、來源可靠性、成本與資安風險",
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"professional_role": "獨立審核、反例檢查、整合風險評分",
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"review_boundary": "只能輸出 blocked / candidate / owner_review，不得直接執行寫入。"
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}
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],
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"report_contract": {
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"agent_auto_allowed_for": [
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"官方來源只讀監控",
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"版本與文件 hash 比對",
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"審核佇列分類",
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"繁中 no-send 報告草稿",
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"離線 replay fixture 準備",
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"低風險文件與讀回 snapshot 更新提案"
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],
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"api_endpoint": "/api/v1/agents/ai-technology-radar-readback",
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"daily": "每日彙整變更、來源失敗、審核佇列與可自動處理項目。",
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"frontend_target": "/zh-TW/governance?tab=agent-market",
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"human_review_required_for": [
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"新 SDK / package / MCP server 安裝",
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"付費 API 或 token 上限變更",
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"模型 provider / 生產路由切換",
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"Telegram Bot 即時發送或審批按鈕策略變更",
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"主機、K8s、workflow、Nginx、secret 或資料層寫入",
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"OpenClaw 生產決策核心替換、拆分或降級"
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],
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"monthly": "每月進行策略 review，決定納入 roadmap、維持 watch-only 或移出監控。",
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"near_real_time": "每 6 小時讀取 primary sources，偵測主流 AI 技術版本、文件與 release 變更。",
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"schedule_cron_utc": "0 */6 * * *",
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"schedule_enabled": true,
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"schedule_workflow": ".gitea/workflows/ai-technology-watch.yaml",
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"weekly": "每週做技術 scorecard，決定 sandbox / replay / adapter design 優先級。"
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},
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"rolling_update_controls": [
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{
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"agent_auto_action": "讀取官方文件、PyPI、npm、GitHub release、primary source hash。",
|
||
"cadence": "每 6 小時",
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"cadence_source": "每 6 小時執行一次只讀 primary-source 檢查，偵測主流 AI 技術版本、文件與 release 變更。",
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"gate": "read_only_only",
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"output": "AI 技術 watch report、來源失敗清單、review queue。"
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},
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||
{
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"agent_auto_action": "依 business applicability、成本、依賴、資安、AWOOOI fit 分類。",
|
||
"cadence": "每日",
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"cadence_source": "每日彙整變更技術，依商業適用性、依賴風險、成本風險與資安風險分組。",
|
||
"gate": "no_send_report_until_delivery_gate",
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||
"output": "日報摘要與中低風險自動處理建議。"
|
||
},
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||
{
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"agent_auto_action": "刷新 scorecard，決定 sandbox / replay / adapter design 優先級。",
|
||
"cadence": "每週",
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||
"cadence_source": "每週刷新技術 scorecard，判斷是否值得進入 sandbox、offline replay 或 adapter design。",
|
||
"gate": "scorecard_required_before_replay",
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||
"output": "週報、優先序、候選整合審查包。"
|
||
},
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||
{
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"agent_auto_action": "彙整趨勢，提出 roadmap / watch-only / retire 建議。",
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"cadence": "每月",
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"cadence_source": "每月策略檢討，決定技術應納入 roadmap、維持 watch-only 或從監控清單移除。",
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||
"gate": "human_review_for_strategy_or_production_change",
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||
"output": "月報與策略審核包。"
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||
}
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],
|
||
"schema_version": "ai_technology_radar_readback_v1",
|
||
"source_scope": {
|
||
"agent_market_radar_readback": "docs/operations/ai-agent-market-radar-readback.snapshot.json",
|
||
"gitea_main_evidence_basis_commit": "683428bd",
|
||
"scope_note": "本讀回只整合已提交的只讀來源監控、AI Agent 市場雷達與治理 gate；不包含 raw chat history、secret、session 或本機工作視窗內容。",
|
||
"technology_source_registry": "docs/ai/ai-technology-watch-sources.v1.json",
|
||
"technology_watch_report": "docs/evaluations/ai_technology_watch_report_2026-06-25.json"
|
||
},
|
||
"summary": {
|
||
"ai_technology_radar_completion_percent": 100.0,
|
||
"changed_technologies": 0,
|
||
"high_priority_count": 14,
|
||
"overall_completion_percent": 42.2,
|
||
"review_queue_count": 0,
|
||
"rolling_update_status": "near_real_time_watch_ready_integration_gated",
|
||
"source_count": 47,
|
||
"source_failures": 0,
|
||
"technology_area_count": 6,
|
||
"technology_count": 20
|
||
},
|
||
"technology_area_counts": {
|
||
"agent_frameworks": 6,
|
||
"evaluation_and_observability": 2,
|
||
"mcp_and_a2a": 2,
|
||
"model_providers": 3,
|
||
"model_serving": 2,
|
||
"rag_and_vector": 5
|
||
},
|
||
"technology_domains": [
|
||
{
|
||
"changed_count": 0,
|
||
"high_priority_count": 5,
|
||
"representative_technologies": [
|
||
"OpenAI Agents SDK",
|
||
"NVIDIA Nemotron + NeMo Agent Toolkit",
|
||
"LangGraph",
|
||
"Google Agent Development Kit"
|
||
],
|
||
"technology_area": "agent_frameworks",
|
||
"technology_count": 6
|
||
},
|
||
{
|
||
"changed_count": 0,
|
||
"high_priority_count": 2,
|
||
"representative_technologies": [
|
||
"Ragas",
|
||
"Langfuse"
|
||
],
|
||
"technology_area": "evaluation_and_observability",
|
||
"technology_count": 2
|
||
},
|
||
{
|
||
"changed_count": 0,
|
||
"high_priority_count": 2,
|
||
"representative_technologies": [
|
||
"Model Context Protocol SDK",
|
||
"Agent2Agent Protocol"
|
||
],
|
||
"technology_area": "mcp_and_a2a",
|
||
"technology_count": 2
|
||
},
|
||
{
|
||
"changed_count": 0,
|
||
"high_priority_count": 3,
|
||
"representative_technologies": [
|
||
"OpenAI Model Platform",
|
||
"Anthropic Claude Platform",
|
||
"Google Gemini Platform"
|
||
],
|
||
"technology_area": "model_providers",
|
||
"technology_count": 3
|
||
},
|
||
{
|
||
"changed_count": 0,
|
||
"high_priority_count": 0,
|
||
"representative_technologies": [
|
||
"Hugging Face Text Generation Inference",
|
||
"vLLM"
|
||
],
|
||
"technology_area": "model_serving",
|
||
"technology_count": 2
|
||
},
|
||
{
|
||
"changed_count": 0,
|
||
"high_priority_count": 2,
|
||
"representative_technologies": [
|
||
"LlamaIndex",
|
||
"LangChain",
|
||
"pgvector",
|
||
"Qdrant"
|
||
],
|
||
"technology_area": "rag_and_vector",
|
||
"technology_count": 5
|
||
}
|
||
]
|
||
}
|