AI Agent Doesn't Need a Bigger Prompt. It Needs a Data Catalog
It Needs a Data Catalog.I was building an AI agent that could answer questions about company data. It could call tools, generate SQL, execute queries, summarize results, and explain its reasoning. Then I asked it a simple question: What was our monthly recurring revenue from new customers last quarter?
- ▪It Needs a Data Catalog.I was building an AI agent that could answer questions about company data.
- ▪It could call tools, generate SQL, execute queries, summarize results, and explain its reasoning.
- ▪Then I asked it a simple question: What was our monthly recurring revenue from new customers last quarter?
Hacker News (AI / LLM) files mainly under ai. We currently carry 4,692 of its stories.
Story provenance
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Github |
| Canonical URL | https://aeroscissorz.github.io/aeroscissorz/posts/knowledge-catalog/ |
| Publication time | Sun, 13 Sep 2026 18:40:48 +0000 |
| Retrieval time | 2026-09-13T18:51:50.921Z |
| Last seen | 2026-09-13T18:51:50.921Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | advxhumef5Pd · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Your AI Agent Doesn't Need a Bigger Prompt. It Needs a Data Catalog.I was building an AI agent that could answer questions about company data. It looked impressive. It could call tools, generate SQL, execute queries, summarize results, and explain its reasoning. Then I asked it a simple question: What was our monthly recurring revenue from new customers last quarter? The SQL was valid. The query ran successfully. The answer was wrong. That was the real problem. An AI agent can know how to query a database without understanding what the data means. A database schema isn't enough A database can tell an agent that a table contains: customers orders subscriptions revenue refunds It can tell the model that revenue is a numeric column.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Github.
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