Why AI Agents Need a Two-Tier Architecture
Let us start with a problem statement, You have to deploy a public facing chatbot, the bot is supposed to be capable of executing various tools, for example ffmpeg. Now the simplest solution is to just host an app on your server which simply accepts a prompt, decides which tools to call, executes the code on the server itself & returns the output. Take an example, an app which lets you run ffmpeg command using a text prompt.
- ▪Let us start with a problem statement, You have to deploy a public facing chatbot, the bot is supposed to be capable of executing various tools, for example ffmpeg.
- ▪Now the simplest solution is to just host an app on your server which simply accepts a prompt, decides which tools to call, executes the code on the server itself & returns the output.
- ▪Take an example, an app which lets you run ffmpeg command using a text prompt.
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| Original publisher | InstaVM |
| Canonical URL | https://instavm.io/blog/why-ai-agents-need-a-two-tier-architecture |
| Publication time | Fri, 24 Jul 2026 18:42:30 +0000 |
| Retrieval time | 2026-07-24T18:59:21.040Z |
| Last seen | 2026-07-24T18:59:21.040Z |
| 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 | eVxvZZb5xg88 |
| 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 |
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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
What problem are we solving? Let us start with a problem statement, You have to deploy a public facing chatbot, the bot is supposed to be capable of executing various tools, for example ffmpeg. Now the simplest solution is to just host an app on your server which simply accepts a prompt, decides which tools to call, executes the code on the server itself & returns the output. This is an inherently problematic approach. Take an example, an app which lets you run ffmpeg command using a text prompt. User enters "delete the lib ffmpeg" In the above solution it will eventually run the instructed command no matter how robust the system instruction is. Final result, all the users are affected.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at InstaVM.
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