> ## Documentation Index
> Fetch the complete documentation index at: https://docs.metacogna.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Backend Agents

> Specialized agent worker implementations

Each agent worker is a specialized Cloudflare Worker that generates artifacts for a specific phase of software architecture.

## Agent Structure

All agents follow a consistent structure:

```
{agent-name}/
├── src/
│   ├── index.ts           # Main worker handler
│   └── systemPrompts.ts   # Agent system prompt
├── package.json
└── wrangler.toml
```

## Agent Interface

All agents implement the same interface:

```typescript theme={null}
export default {
  async fetch(request: Request, env: Env): Promise<Response> {
    // 1. Parse request body
    // 2. Extract context and user input
    // 3. Call LLM with system prompt
    // 4. Format response
    // 5. Return artifact
  }
};
```

## System Prompts

Each agent has a specialized system prompt defining:

* **Role**: Agent's expertise (e.g., "Senior Product Manager")
* **Goal**: Primary objective
* **Instructions**: Step-by-step process
* **Output Structure**: Required format
* **Constraints**: Rules and limitations

### PRD Agent Prompt

Focuses on:

* Executive summary
* User personas
* Functional requirements
* MoSCoW roadmap

### Data Agent Prompt

Focuses on:

* Entity identification
* Relationship modeling
* Database schema design
* Migration strategies

### Design Agent Prompt

Focuses on:

* Design tokens
* Component library
* UI/UX patterns
* Accessibility

### Logic Agent Prompt

Focuses on:

* Business rules
* State machines
* Validation logic
* **Triggers HITL interrupt**

### API Agent Prompt

Focuses on:

* RESTful API design
* Resource modeling
* Request/response schemas
* OpenAPI specification

### Frontend Agent Prompt

Focuses on:

* Component architecture
* State management
* Routing structure
* Performance optimization

### Deployment Agent Prompt

Focuses on:

* Infrastructure as Code
* CI/CD pipelines
* Monitoring and observability
* Security configuration

## Request Format

Agents receive requests with:

```typescript theme={null}
{
  system_state: {
    current_phase: string;
    project_id: string;
  };
  context: string;        // Previous artifacts
  user_input: string;     // Product seed or phase input
}
```

## Response Format

Agents return responses in:

```typescript theme={null}
{
  system_state: {
    current_phase: string;
    status: 'complete' | 'interrupted';
    interrupt_signal?: boolean;
  };
  artifact: {
    type: 'markdown' | 'code';
    content: string;
  };
  trace: {
    agent: string;
    timestamp: string;
  };
}
```

## LLM Integration

Agents use Gemini API for LLM calls:

```typescript theme={null}
import { GoogleGenerativeAI } from '@google/generative-ai';

const genAI = new GoogleGenerativeAI(env.GEMINI_API_KEY);
const model = genAI.getGenerativeModel({ model: 'gemini-pro' });

const result = await model.generateContent(prompt);
const response = result.response.text();
```

## Error Handling

Agents include error handling:

```typescript theme={null}
try {
  // Agent execution
} catch (error) {
  return new Response(JSON.stringify({
    error: error.message,
    system_state: { status: 'error' }
  }), { status: 500 });
}
```

## Service Bindings

Agents are called via Cloudflare service bindings:

```typescript theme={null}
// In supervisor wrangler.toml
[[services]]
binding = "PRD_AGENT"
service = "parti-prd-agent"
```

## Related Documentation

* [Backend Supervisor](/parti-architecture/backend/supervisor) - Orchestration
* [Agent API](/parti-architecture/api/agents) - API reference
* [Design Architecture](/parti-architecture/design/architecture) - System design