/agentic-development

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)

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)

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/
├── src/
│   ├── agents/
│   │   ├── orchestrator.ts    # Main agent coordinator
│   │   ├── specialized/       # Task-specific agents
│   │   │   ├── researcher.ts
│   │   │   ├── coder.ts
│   │   │   └── reviewer.ts
│   │   └── base.ts            # Shared agent interface
│   ├── tools/
│   │   ├── definitions/       # Tool schemas
│   │   ├── implementations/   # Tool logic
│   │   └── registry.ts        # Tool discovery
│   ├── prompts/
│   │   ├── system/            # Agent instructions
│   │   └── templates/         # Task templates
│   └── memory/
│       ├── conversation.ts    # Short-term context
│       └── persistent.ts      # Long-term storage
├── tests/
│   ├── agents/                # Agent behavior tests
│   ├── tools/                 # Tool unit tests
│   └── evals/                 # End-to-end evaluations
└── skills/                    # Agent skills (Anthropic pattern)
    ├── skill-name/
    │   ├── instructions.md
    │   ├── scripts/
    │   └── resources/

---

Workflow Pattern: Explore-Plan-Execute-Verify

1. Explore Phase

// Gather context before acting
async function explore(task: Task): Promise<Context> {
  const relevantFiles = await agent.searchCodebase(task.query);
  const existingPatterns = await agent.analyzePatterns(relevantFiles);
  const dependencies = await agent.identifyDependencies(task);

  return { relevantFiles, existingPatterns, dependencies };
}

2. Plan Phase (Critical)

// Plan explicitly before execution
async function plan(task: Task, context: Context): Promise<Plan> {
  const prompt = `
    Task: ${task.description}
    Context: ${JSON.stringify(context)}

    Create a step-by-step plan. For each step:
    1. What action to take
    2. What tools to use
    3. How to verify success
    4. What could go wrong

    Output JSON with steps array.
  `;

  return await llmCall({ prompt, schema: PlanSchema });
}

3. Execute Phase

// Execute with verification at each