Jerry Liu：数据层需重大突破才能支撑Agent智能搜索

There's got to be some major advances in the data ingestion, indexing, and retrieval layer to make t...

There's got to be some major advances in the data ingestion, indexing, and retrieval layer to make this happen. Agents are getting much more sophisticated in fetching diverse sources of context with simple tool interfaces (e.g. CLI + filesystem). But: * It is a known problem that agentic federated search through MCP isn't optimal for retrieval relevance * Agentic search is generally really slow without pre-indexing * When you interleave diverse sources of data, you need to weight each item properly * Different types of data require different query interfaces - from SQL to embedding search Looking forward to seeing how this materializes. Our main focus today is unlocking document-based context and we'd be keen to see how this integrates with SaaS/structured data. John Suh @john_ssuh Increasingly, I believe companies may need to be rebuilt from the ground up, where you have a single timeline of all observability + product metrics + file changes laid out in a retrievable system, like Datadog + Posthog + Google Drive + Slack (really unified filesystem of Claude Code chats + Codex chats). This might be the new data foundation for any and all companies to maximize AI. Needs to be rebuilt because keeping track of diffs on existing system basically impossible to produce longitudinal information on decisions and rollbacks, something coding agent storage companies are actively trying to figure out, but this should extend to businesses as a whole. Highly skeptical existing businesses will adopt this though because it means overhauling everything about their instrumentation and business data, but I think businesses built on this foundation probably can execute 100x better and faster 🔗 View Quoted Tweet 💬 5 🔄 0 ❤️ 8 👀 929 📊 5 ⚡