# AI Agents Suck at Backends (How to Fix)

Source: https://www.youtube.com/watch?v=6RQHVz8MJI4
Recap page: https://rapidrecap.app/video/6RQHVz8MJI4
Generated: 2026-02-22T14:04:44.594+00:00

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## Quick Overview

The primary reason AI coding agents struggle with backends is the complexity, fragmentation, and lack of standardized documentation across various backend services like databases, authentication, and storage, leading to agents wasting tokens, hallucinating, and getting stuck in retry loops requiring manual developer debugging, which the InsForge backend platform aims to fix by providing a unified, agent-centric semantic layer over these primitives.

**Key Points:**
- The promised simplicity of AI coding agents (Prompt Agent -> AI Builds App -> Deployed App) contrasts sharply with reality, where backend complexity causes agents to fail during configuration and debugging.
- The backend complexity stems from each service (Database, Auth, Storage, etc.) having different SDKs, documentation, and configurations, causing agents to waste tokens and enter error loops.
- AI coding agents like Cursor/Claude/Codex handle frontend tasks (React, HTML, CSS) relatively well because these environments are generally well-documented and consistent.
- InsForge is introduced as a solution: a backend development platform that acts as a semantic layer connecting AI coding agents (Cursor, Claude, Codex) to backend primitives (Postgres DB, Auth, Storage, Functions, etc.).
- InsForge's documentation shows it supports multiple LLM providers via a Model Gateway and provides structured logging and database visualization to help agents understand the infrastructure.
- The demonstration shows the successful integration of an agentic file search project ('agentic-file-search') using InsForge, where the agent successfully queries documents using semantic search and retrieves specific financial data ($43,330,000).

![Screenshot at 00:13: The slide contrasts the promised simple workflow \(Developer prompts agent -\> AI builds app -\> Full app deployed\) with the complex reality where the agent builds UI/Frontend successfully but gets stuck in error/retry loops when configuring the backend services \(Database, Auth, Storage, etc.\).](https://ss.rapidrecap.app/screens/6RQHVz8MJI4/00-00-13.jpg)

**Context:** This video addresses the frustration developers feel when trying to build full-stack applications using AI coding agents, highlighting that while agents excel at frontend tasks, they frequently fail when dealing with the backend infrastructure. The speaker contrasts the idealized workflow with the actual, error-prone reality involving complex, disparate services like databases and authentication. The solution presented is InsForge, described as 'The backend for Agentic Engineering,' designed to abstract this complexity behind a unified semantic layer understandable by AI agents.

## Detailed Analysis

The video explains that the current 'vibe' promise of AI coding agents—where a developer prompt instantly yields a deployed application—is often broken by backend complexity. While agents handle frontend development (React, HTML, CSS) quite well due to consistent documentation, they struggle severely with backends because services like databases, authentication, and storage each require unique SDKs, diverse documentation, and complex configuration, leading to agents wasting tokens, hallucinating, and getting stuck in infinite retry loops that necessitate manual debugging. The proposed solution is InsForge, which acts as a backend development platform built specifically for agentic coding. It exposes backend primitives (Database, Auth, Storage, Functions, Model Gateway) through a unified 'InsForge Semantic Layer' that agents can understand, reason about, and operate end-to-end. The demonstration shows the setup process, including installing the agent integration (Codex was chosen), viewing the resulting Postgres database schema (corpora, documents, chunks, schemas, chunk_embeddings), and successfully querying the system using the agentic file search tool ('fs-explorer') to find the purchase price for an acquisition ($43,330,000) by navigating semantic search and document retrieval steps.

### The Vibe Coding Promise vs. Reality

- Promise shows simple flow; Reality shows agent stuck in configuration errors on backend setup (Database, Auth, Storage) requiring manual debugging
- Frontend (React, HTML, CSS) works well; Backend is the hard part.

### What a 'Real App' Needs

- AI Coding Agents struggle with the backend due to complexity; each service has different SDKs, documentation, and configurations, leading to wasted tokens and error loops.

### The Solution

- InsForge is introduced as 'The backend for Agentic Engineering,' providing a semantic layer between the AI Coding Agent (Cursor, Claude, Codex) and the backend primitives (Postgres DB, Auth, Storage, Functions, Model Gateway).

### InsForge Backend Components

- Core products include Authentication (Email/Password, OAuth), Postgres relational database, S3 compatible storage, Model Gateway (OpenAI compatible API across multiple LLM providers), Edge Functions, and Site Deployment.

### Demonstration Workflow

- Project setup for 'agentic-file-search' using Codex 3, followed by reviewing the schema in the database visualization (corpora, documents, chunks, schemas, chunk_embeddings), confirming LLM connectivity via the Model Gateway (Gemini, Anthropic, DeepSeek enabled), and finally executing a semantic search query about an acquisition price ($43,330,000) successfully.

![Screenshot at 00:03: The initial slide contrasting the 'Promise' \(simple AI build flow\) against 'The Reality' \(agent failure loop during backend setup\).](https://ss.rapidrecap.app/screens/6RQHVz8MJI4/00-00-03.jpg)
![Screenshot at 00:26: Step 3 highlights where the process breaks: the backend requires configuring multiple services \(Database, Auth, File Storage, etc.\), each having different SDKs and documentation.](https://ss.rapidrecap.app/screens/6RQHVz8MJI4/00-00-26.jpg)
![Screenshot at 01:13: A diagram illustrating that the AI Coding Agent struggles with the entire backend stack because 'Each service = different SDK, config, docs' leading to wasted tokens and loops.](https://ss.rapidrecap.app/screens/6RQHVz8MJI4/00-01-13.jpg)
![Screenshot at 02:00: The proposed solution architecture where InsForge acts as a semantic layer connecting the AI Coding Agent to all necessary backend services \(Postgres DB, Auth, Storage, Functions, Realtime, AI Models, Deploy\).](https://ss.rapidrecap.app/screens/6RQHVz8MJI4/00-02-00.jpg)
![Screenshot at 08:23: The demonstration of the agentic file search tool \('fs-explorer'\) successfully executing a semantic search query and retrieving the specific purchase price of $43,330,000 from the indexed documents.](https://ss.rapidrecap.app/screens/6RQHVz8MJI4/00-08-23.jpg)