从技术验证到业务落地：智能体的下一步该怎么走
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Merged summary
TL;DR - A Shenzhen industry forum examined how AI agents can move from demos into dependable enterprise deployments. Speakers emphasized context and memory infrastructure, controlled knowledge engineering, low-code orchestration, and delivery of usable business outputs.
- Long-running, multi-step agents need engineered memory and context management to remain coherent and consistent.
- Enterprise adoption depends on overcoming hallucinations and misuse through granular control of knowledge structures.
- Visual orchestration, multimodal perception, private deployment, and end-to-end governance can lower implementation and operational barriers.
- Vertical agents increasingly compete on the quality of completed deliverables—not merely their conversational ability.
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从技术验证到业务落地：智能体的下一步该怎么走
Public signals
N/A
TL;DR - A Shenzhen industry forum examined how AI agents can move from demos into dependable enterprise deployments. Speakers emphasized context and memory infrastructure, controlled knowledge engineering, low-code orchestration, and delivery of usable business outputs.
- Long-running, multi-step agents need engineered memory and context management to remain coherent and consistent.
- Enterprise adoption depends on overcoming hallucinations and misuse through granular control of knowledge structures.
- Visual orchestration, multimodal perception, private deployment, and end-to-end governance can lower implementation and operational barriers.
- Vertical agents increasingly compete on the quality of completed deliverables—not merely their conversational ability.