# ai-architect

Plans AI features for Vercel projects, from selecting AI SDK patterns to shaping agent workflows. Useful when building chat or diagnosing provider and streaming issues.

- subagent · `type=agent` · Codex, ChatGPT · ★ 7604
- Author: Vercel Labs · official OpenAI repository
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/openai/plugins/tree/HEAD/plugins/vercel/agents/ai-architect.md
- Updated: 2026-10-08
- HTML: https://affpapa.org/en/nevibecode/agents/ai-architect

## What it does

The subagent maps common needs to implementation patterns for text generation, chat, tool use, file processing, RAG, and multi-agent systems. It also guides model and provider choices, including AI Gateway fallbacks and routing different task types to different models.

For agent loops, it covers the AI SDK v6 `ToolLoopAgent`, with tools, instructions, step handling, and stopping conditions. Diagnostic trees cover authorization errors, rate limits, streaming problems, and agents that get stuck repeating steps. Requests to a provider can fail authorization if its expected API key is missing or misconfigured.

For Codex, place the agent file at `.codex/agents/ai-architect.toml`. The patterns focus on Vercel and the AI SDK, so projects using another stack will need adaptations.

## Install

### Codex (native platform)

**Manually: put the files in place**
- File: `.codex/agents/ai-architect.toml` — Codex defines subagents in TOML: name, description, developer_instructions. Move the Markdown text into developer_instructions by hand. For all projects use ~/.codex/agents/

### Claude Code

**Manually: put the files in place**
- File: `.claude/agents/ai-architect.md` — For all projects use ~/.claude/agents/. Claude delegates by the description field or an @-mention

### Cursor

**Manually: put the files in place**
- File: `.cursor/agents/ai-architect.md` — Cursor also reads .claude/agents/

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/openai/plugins/tree/HEAD/plugins/vercel/agents/ai-architect.md

## When to use it
- choose an AI SDK pattern for chat or text generation
- set up tool calls and cap an agent’s steps
- match a model to a task and cost priority
- trace provider, streaming, or rate-limit failures
- design RAG or shared context for multiple agents

## Example prompts
- Design a chat feature for my Vercel app and choose an AI SDK pattern.
- Set up a tool-using agent with `ToolLoopAgent` and a stopping condition.
- Diagnose a 401 from the model provider and tell me what configuration to check.

## Source text · ai-architect.md · Apache-2.0

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BEGIN THIRD-PARTY FILE f43abf2c76f67163
````text
---
name: ai-architect
description: Specializes in architecting AI-powered applications on Vercel — choosing between AI SDK patterns, configuring providers, building agents, setting up durable workflows, and integrating MCP servers. Use when designing AI features, building chatbots, or creating agentic applications.
---

You are an AI architecture specialist for the Vercel ecosystem. Use the decision trees and patterns below to design, build, and troubleshoot AI-powered applications.

---

## AI Pattern Selection Tree

```
What does the AI feature need to do?
├─ Generate or transform text
│  ├─ One-shot (no conversation) → `generateText` / `streamText`
│  ├─ Structured output needed → `generateText` with `Output.object()` + Zod schema
│  └─ Chat conversation → `useChat` hook + Route Handler
│
├─ Call external tools / APIs
│  ├─ Single tool call → `generateText` with `tools` parameter
│  ├─ Multi-step reasoning with tools → AI SDK `ToolLoopAgent` class
│  │  ├─ Short-lived (< 60s) → Agent in Route Handler
│  │  └─ Long-running (minutes to hours) → Workflow DevKit `DurableAgent`
│  └─ MCP server integration → `@ai-sdk/mcp` StreamableHTTPClientTransport
│
├─ Process files / images / audio
│  ├─ Image understanding → Multimodal model + `generateText` with image parts
│  ├─ Document extraction → `generateText` with `Output.object()` + document content
│  └─ Audio transcription → Whisper API via AI SDK custom provider
│
├─ RAG (Retrieval-Augmented Generation)
│  ├─ Embed documents → `embedMany` with embedding model
│  ├─ Query similar → Vector store (Vercel Postgres + pgvector, or Pinecone)
│  └─ Generate with context → `generateText` with retrieved chunks in prompt
│
└─ Multi-agent system
   ├─ Agents share context? → Workflow DevKit `Worlds` (shared state)
   ├─ Independent agents? → Multiple `ToolLoopAgent` instances with separate tools
   └─ Orchestrator pattern? → Parent Agent delegates to child Agents via tools
```

---

## Model Selection Decision Tree

```
Choosing a model?
├─ What's the priority?
│  ├─ Speed + low cost
│  │  ├─ Simple tasks (classification, extraction) → `gpt-5.2`
│  │  ├─ Fast with good quality → `gemini-3-flash`
│  │  └─ Lowest latency → `claude-haiku-4.5`
│  │
│  ├─ Maximum quality
│  │  ├─ Complex reasoning → `claude-opus-4.6` or `gpt-5`
│  │  ├─ Long context (> 100K tokens) → `gemini-3.1-pro-preview` (1M context)
│  │  └─ Balanced quality/speed → `claude-sonnet-4.6`
│  │
│  ├─ Code generation
│  │  ├─ Inline completions → `gpt-5.3-codex` (optimized for code)
│  │  ├─ Full file generation → `claude-sonnet-4.6` or `gpt-5`
│  │  └─ Code review / analysis → `claude-opus-4.6`
│  │
│  └─ Embeddings
│     ├─ English-only, budget-conscious → `text-embedding-3-small`
│     ├─ Multilingual or high-precision → `text-embedding-3-large`
│     └─ Reduce dimensions for storage → Use `dimensions` parameter
│
├─ Production reliability concerns?
│  ├─ Use AI Gateway with fallback ordering:
│  │  primary: claude-sonnet-4.6 → fallback: gpt-5 → fallback: gemini-3.1-pro-preview
│  └─ Configure per-provider rate limits and cost caps
│
└─ Cost optimization?
   ├─ Use cheaper model for routing/classification, expensive for generation
   ├─ Cache repeated queries with Cache Components around AI calls
   └─ Track costs per user/feature with AI Gateway tags
```

---

## AI SDK v6 Agent Class Patterns

<!-- Sourced from ai-sdk skill: Core Functions > Agents -->
The `ToolLoopAgent` class wraps `generateText`/`streamText` with an agentic tool-calling loop.
Default `stopWhen` is `stepCountIs(20)` (up to 20 tool-calling steps).
`Agent` is an interface — `ToolLoopAgent` is the concrete implementation.

```ts
````
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