RAG Isn't an Agent — I Built the Layer Between Retrieval and Action
LLM ApplicationsRAG Isn't an Agent — I Built the Layer Between Retrieval and ActionRAG retrieves. I built both separately, connected them explicitly, and ran the same nine tasks through all three systems.Emmimal P AlexanderSeptember 25, 202619 min readTL;DRI built the whole thing in pure Python and ran the tests myself, with the results shown as pass or fail.RAG can find information. I wanted to see what actually happens when you put them together instead of assuming both are needed.I built three versions: retrieval only, a deterministic action planner, and a hybrid system that connects retrieval with actions.
- ▪LLM ApplicationsRAG Isn't an Agent — I Built the Layer Between Retrieval and ActionRAG retrieves.
- ▪I built both separately, connected them explicitly, and ran the same nine tasks through all three systems.Emmimal P AlexanderSeptember 25, 202619 min readTL;DRI built the whole thing in pure Python and ran the tests myself, with the results
- ▪I wanted to see what actually happens when you put them together instead of assuming both are needed.I built three versions: retrieval only, a deterministic action planner, and a hybrid system that connects retrieval with actions.
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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 | Towards Data Science |
| Canonical URL | https://towardsdatascience.com/rag-isnt-an-agent-i-built-the-layer-between-retrieval-and-action/ |
| Publication time | Fri, 25 Sep 2026 12:30:01 GMT |
| Retrieval time | 2026-09-25T12:35:32.726Z |
| Last seen | 2026-09-25T12:35:32.726Z |
| 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 | Pt3gjU2ZAuES · 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
LLM ApplicationsRAG Isn't an Agent — I Built the Layer Between Retrieval and ActionRAG retrieves. Agents act. I built both separately, connected them explicitly, and ran the same nine tasks through all three systems.Emmimal P AlexanderSeptember 25, 202619 min readTL;DRI built the whole thing in pure Python and ran the tests myself, with the results shown as pass or fail.RAG can find information. An agent can take action. I wanted to see what actually happens when you put them together instead of assuming both are needed.I built three versions: retrieval only, a deterministic action planner, and a hybrid system that connects retrieval with actions. Then I ran the same nine tasks through all three.I found two real bugs while building it.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Towards Data Science.
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