# Product Catalog Enrichment Agent

> A catalog agent that enriches product descriptions, attributes, and categories from your source data, flags missing specs for review, and never fabricates claims.

- **Category:** E-commerce Ops
- **Difficulty:** Intermediate
- **Setup time:** 40 min
- **Stacks:** Claude, LangGraph, OpenAI
- **Version:** 2.0.0 (updated 2026-06-21)
- **Source:** https://agent-kits.com/kit/product-catalog-enricher

## Overview

Turns thin product data into rich descriptions, attributes, categories, and SEO fields.

Builds every entry strictly from the source data you provide.

Flags missing or uncertain specs for review instead of guessing them.

Defensive: never fabricates specs or compatibility/safety claims, and keeps the taxonomy consistent.

## When to use it

- You have thin or inconsistent product data to enrich at scale.
- You want descriptions and attributes grounded in real source data.
- You want missing specs flagged, not invented.
- You want consistent categorization and SEO fields.

## When not to use it

- You want it to invent specs or claims to fill gaps — it won't.
- You have no source product data for it to enrich from.
- You can't review flagged uncertain attributes.
- You need fabricated reviews or ratings (it won't produce them).

## Architecture

- **Source intake** — Takes the provided product/source data that is the only allowed basis for enrichment.
- **Attribute extractor** — Pulls structured attributes from the source, mapping them to the attribute schema.
- **Description writer** — Writes a readable, accurate description grounded strictly in the source data.
- **Categorizer** — Assigns the product to the taxonomy consistently with similar items.
- **Claim guard** — Blocks fabricated compatibility, safety, and performance claims, flagging any that need verification.
- **Gap flagger** — Marks missing or uncertain attributes for review rather than guessing a spec.
- **SEO field generator** — Produces SEO title and meta fields from the grounded content.

## Files in this bundle

- `system-prompt.md` — the agent's system prompt
- `tools.json` — tool/function definitions the agent expects
- `workflow.md` — step-by-step run loop
- `.env.example` — environment variables to fill in
- `kit.json` — full structured metadata
- `starters/` — minimal starter snippets
- `LICENSE` — Apache-2.0 (code, configuration, and schema)
- `LICENSE-CONTENT` — CC BY 4.0 (specification text and documentation)
- `NOTICE` — attribution and trademark notice
- `run.py` — **runnable demo**: real agent loop with mock tools (`pip install anthropic`, set `ANTHROPIC_API_KEY`, `python run.py`)
- `agentaz.json` / `agentaz.schema.json` — AgentAz™ governance spec + schema

## Implementation notes

- Enrich strictly from source data and never invent specs like dimensions, materials, or weight; a guessed spec on a live product page causes returns and erodes trust.
- Never fabricate compatibility, safety, or regulatory claims ('works with all', 'FDA approved'); these carry direct legal and returns risk, so flag them for verification instead.
- Never invent reviews, ratings, awards, or other social proof.
- Flag missing or uncertain attributes as null for review rather than filling them with plausible guesses.
- Keep categorization and attribute naming consistent with the taxonomy so the catalog stays clean and filterable.
- Stay faithful to the source meaning; enrich the wording, not the facts.
- A cheaper model is usually enough to format and categorize clean entries, so keep the strong model for ambiguous data and claim-guarding.

## Variations

- **Basic — Description enricher:** Writes source-grounded descriptions and extracts attributes for catalog entries. On demand.
- **Advanced — Guarded enrichment:** Adds taxonomy categorization, SEO fields, no-fabricated-spec/claim guards, and missing-attribute flagging for review.
- **Enterprise — Catalog operations:** Adds PIM integration, batch enrichment, taxonomy governance, claim-verification workflows, and audit trails across the catalog.

## FAQ

**Will it invent specs to fill gaps?**

No. It enriches only from your source data. If a spec like dimensions or fabric composition is missing, it leaves the field null and flags it for review rather than guessing, because a wrong spec on a product page drives returns and complaints.

**Can it add marketing claims?**

Only ones your source supports. It won't fabricate compatibility, safety, or regulatory claims like 'works with all models' or 'FDA approved' — those carry legal and returns risk. It flags requested claims for verification and adds real, source-backed ones.

**Does it make up reviews or ratings?**

Never. It won't invent reviews, ratings, awards, or testimonials. Social proof has to come from real data, not the agent.

**Will the catalog stay consistent?**

Yes. It applies your taxonomy and attribute schema consistently across products, so categorization and attribute naming stay clean and filterable.

**Is it faithful to my data?**

Yes. It enriches the wording and structure, not the facts. It preserves the meaning of your source data and won't change a value to make a product sound better.

**Can it generate SEO fields?**

Yes. It produces SEO titles and meta descriptions from the grounded content, so listings are search-friendly without introducing unsupported claims.


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© 2026 AgentKits · www.agent-kits.com

**Open source.** Code, configuration, and the AgentAz™ schema are licensed under
**Apache-2.0**; specification text and documentation under **CC BY 4.0** (with
attribution to AgentKits, link to https://www.agent-kits.com). See LICENSE, LICENSE-CONTENT,
and NOTICE in this bundle. "AgentAz" and "AgentKits" are trademarks of AgentKits.

**Flagship reference blueprint.** View the source, AgentAz™ spec, and frozen v1.0 schema
in the public registry: https://github.com/agent-kits/agentaz
Original: https://agent-kits.com/kit/product-catalog-enricher
