1. Value Creation on Existing Software
- Unified chat interface reduces context-switching
- Automated, trigger-based workflows (“say once, do always”)
- Knowledge-driven outputs with continuous learning loops
- Scalable plugin ecosystem for third-party integrations
真正的工具只是自然语言的转换接口
2. Key Application Technologies
- Natural Language Understanding & Dialogue Management
- Workflow orchestration (triggers, branching, retries)
- Retrieval-Augmented Generation (RAG) for content accuracy
- Plugin invocation framework (OpenAPI/gRPC, sandboxing)
- Low-code visual builders (flow & dialogue editors)
3. Key Foundational Technologies
- Transformer-based LLMs and fine-tuning pipelines
- Vector databases and similarity search engines
- Distributed workflow engines (Temporal, Airflow)
- Containerized microservices (Kubernetes, Docker)
- API gateways and service meshes (Istio, Envoy)
- Observability stacks (Prometheus, Jaeger)
4. Current Challenges
- NLU robustness, multi-turn context, domain adaptation
- Ensuring vector index freshness and retrieval consistency
- Maintaining third-party connectors amid API churn
- End-to-end observability and fault-recovery at scale
- Data privacy, compliance, and secure model/data handling
- Cost-efficient inference and infrastructure resource control