---
name: delegate-to-agent
description: >-
  How to delegate all AI work to the agent chat. Use when delegating AI work
  from UI or scripts to the agent, when a user asks for agent behavior or
  LLM-powered features, when tempted to add inline LLM calls, or when sending
  messages to the agent from application code.
---

# Delegate All AI to the Agent

## Rule

The UI and server never call an LLM directly. All AI work is delegated to the agent through the chat bridge.

## Why

The agent is the single AI interface. It has context about the full project, can read/write any file, and can run scripts. Inline LLM calls bypass this — they create a shadow AI that doesn't know what the agent knows and can't coordinate with it.

## How

**From the UI (client):**

```ts
import { sendToAgentChat } from "@agent-native/core";

sendToAgentChat({
  message: "Generate a summary of this document",
  context: documentContent, // optional hidden context (not shown in chat UI)
  submit: true, // auto-submit to the agent
});
```

**From scripts (Node):**

```ts
import { agentChat } from "@agent-native/core";

agentChat.submit("Process the uploaded images and create thumbnails");
```

**From the UI, detecting when agent is done:**

```ts
import { useAgentChatGenerating } from "@agent-native/core";

function MyComponent() {
  const isGenerating = useAgentChatGenerating();
  // Show loading state while agent is working
}
```

## `submit` vs Prefill

The `submit` option controls whether the message is sent automatically or placed in the chat input for user review:

| `submit` value | Behavior                                | Use when                                                                            |
| -------------- | --------------------------------------- | ----------------------------------------------------------------------------------- |
| `true`         | Auto-submits to the agent immediately   | Routine operations the user has already approved                                    |
| `false`        | Prefills the chat input for user review | High-stakes operations (deleting data, modifying code, API calls with side effects) |
| omitted        | Uses the project's default setting      | General-purpose delegation                                                          |

```ts
// Auto-submit: routine operation
sendToAgentChat({ message: "Update the project summary", submit: true });

// Prefill: let user review before sending
sendToAgentChat({
  message: "Delete all projects older than 30 days",
  submit: false,
});
```

## Capture user input first when generating from a prompt

Buttons that produce new content ("New Design", "Create Dashboard", "Make Deck", "Generate Form") need the user's prompt as input. **Never hardcode a generic message** — the result will be a generic generation the user didn't actually ask for.

**Bad** — auto-submits a placeholder message; the user never said what they wanted:

```tsx
<Button
  onClick={() =>
    sendToAgentChat({ message: "make a design", submit: true })
  }
>
  New Design
</Button>
```

**Good** — Popover anchored to the button captures the prompt, then submits it:

```tsx
<Popover open={open} onOpenChange={setOpen}>
  <PopoverTrigger asChild>
    <Button>New Design</Button>
  </PopoverTrigger>
  <PopoverContent className="w-96">
    <Textarea
      autoFocus
      value={prompt}
      onChange={(e) => setPrompt(e.target.value)}
      placeholder="What do you want to design?"
    />
    <Button
      onClick={() => {
        sendToAgentChat({ message: prompt, submit: true });
        setOpen(false);
        setPrompt("");
      }}
    >
      Create
    </Button>
  </PopoverContent>
</Popover>
```

**Always ask for input first when** the output depends on a prompt the user must provide — "design what?", "deck about what?", "dashboard for which metric?", "form for which use case?".

**Auto-submit without input is fine when intent is unambiguous:**

- "Try to fix" on a tool error — submits the error details with a clear fix instruction
- "Retry the last operation" after a transient failure
- Single-purpose buttons where there is nothing meaningful for the user to add

If you find yourself writing `submit: true` with a hardcoded creative verb (`"design a..."`, `"write a..."`, `"build a..."`), stop and add a Popover.

## Don't

- Don't `import Anthropic from "@anthropic-ai/sdk"` in client or server code
- Don't `import OpenAI from "openai"` in client or server code
- Don't make direct API calls to any LLM provider
- Don't use AI SDK functions like `generateText()`, `streamText()`, etc.
- Don't build "AI features" that bypass the agent chat
- Don't auto-submit a hardcoded prompt for generative actions — capture user input first (see above)

## Exception

Scripts may call external APIs (image generation, search, etc.) — but the AI reasoning and orchestration still goes through the agent. A script is a tool the agent uses, not a replacement for the agent.

## When to Use A2A Instead

`sendToAgentChat()` delegates work to the **local** agent — the one running alongside your app. When the work should go to a **different** agent entirely (e.g., asking an analytics agent for data, or a calendar agent for availability), use the A2A (agent-to-agent) protocol instead.

```ts
import { callAgent } from "@agent-native/core/a2a";

// Call a different agent — not the local agent chat
const stats = await callAgent(
  "https://analytics.example.com",
  "What were last week's signups?",
  { apiKey: process.env.ANALYTICS_A2A_KEY },
);
```

See the **a2a-protocol** skill for the full pattern.

## Related Skills

- **a2a-protocol** — When the work goes to a different agent, not the local one
- **scripts** — The agent invokes scripts via `pnpm action <name>` to perform complex operations
- **self-modifying-code** — The agent operates through the chat bridge to make code changes
- **storing-data** — The agent writes results to the database after processing requests
- **real-time-sync** — The UI updates automatically when the agent writes data
