agentic-development

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---
name: agentic-development
description: Build AI agents with Pydantic AI (Python) and Claude SDK (Node.js)
when-to-use: When building AI agents, tool-using LLM systems, or agentic workflows
user-invocable: false
effort: high
---

# Agentic Development Skill


For building autonomous AI agents that perform multi-step tasks with tools.

**Sources:** [Claude Agent SDK](https://docs.anthropic.com/en/docs/agents-and-tools/claude-agent-sdk) | [Anthropic Claude Code Best Practices](https://www.anthropic.com/engineering/claude-code-best-practices) | [Pydantic AI](https://ai.pydantic.dev/) | [Google Gemini Agent Development](https://developers.googleblog.com/en/building-agents-google-gemini-open-source-frameworks/) | [OpenAI Building Agents](https://developers.openai.com/tracks/building-agents/)

---

## Framework Selection by Language

| Language/Framework | Default | Why |
|-------------------|---------|-----|
| **Python** | **Pydantic AI** | Type-safe, Pydantic validation, multi-model, production-ready |
| **Node.js / Next.js** | **Claude Agent SDK** | Official Anthropic SDK, tools, multi-agent, native streaming |

### Python: Pydantic AI (Default)
```python
from pydantic_ai import Agent
from pydantic import BaseModel

class SearchResult(BaseModel):
    title: str
    url: str
    summary: str

agent = Agent(
    'claude-sonnet-4-20250514',
    result_type=list[SearchResult],
    system_prompt='You are a research assistant.',
)

# Type-safe result
result = await agent.run('Find articles about AI agents')
for item in result.data:
    print(f"{item.title}: {item.url}")
```

### Node.js / Next.js: Claude Agent SDK (Default)
```typescript
import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic();

// Define tools
const tools: Anthropic.Tool[] = [
  {
    name: "web_search",
    description: "Search the web for information",
    input_schema: {
      type: "object",
      properties: {
        query: { type: "string", description: "Search query" },
      },
      required: ["query"],
    },
  },
];

// Agentic loop
async function runAgent(prompt: string) {
  const messages: Anthropic.MessageParam[] = [
    { role: "user", content: prompt },
  ];

  while (true) {
    const response = await client.messages.create({
      model: "claude-sonnet-4-20250514",
      max_tokens: 4096,
      tools,
      messages,
    });

    // Check for tool use
    if (response.stop_reason === "tool_use") {
      const toolUse = response.content.find((b) => b.type === "tool_use");
      if (toolUse) {
        const result = await executeTool(toolUse.name, toolUse.input);
        messages.push({ role: "assistant", content: response.content });
        messages.push({
          role: "user",
          content: [{ type: "tool_result", tool_use_id: toolUse.id, content: result }],
        });
        continue;
      }
    }

    // Done - return final response
    return response.content.find((b) => b.type === "text")?.text;
  }
}
```

---

## Core Principle

**Plan first, act incrementally, verify always.**

Agents that research and plan before executing consistently outperform those that jump straight to action. Break complex tasks into verifiable steps, use tools judiciously, and maintain clear state throughout execution.

---

## Agent Architecture

### Three Components (OpenAI)
```
┌─────────────────────────────────────────────────┐
│                    AGENT                        │
├─────────────────────────────────────────────────┤
│  Model (Brain)      │ LLM for reasoning &       │
│                     │ decision-making           │
├─────────────────────┼───────────────────────────┤
│  Tools (Arms/Legs)  │ APIs, functions, external │
│                     │ systems for action        │
├─────────────────────┼───────────────────────────┤
│  Instructions       │ System prompts defining   │
│  (Rules)            │ behavior & boundaries     │
└─────────────────────┴───────────────────────────┘
```

### Project Structure
```
project/
├── s