agentic-development

Agentic Development Skill

Load with: base.md + llm-patterns.md + [language].md

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

Sources: Claude Agent SDK | Anthropic Claude Code Best Practices | Pydantic AI | Google Gemini Agent Development | OpenAI 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 step
async function execute(plan: Plan): Promise<Result[]> {
  const results: Result[] = [];

  for (const step of plan.steps) {
    // Execute single step
    const result = await executeStep(step);

    // Verify before continuing
    if (!await verify(step, result)) {
      // Self-correct or escalate
      const corrected = await selfCorrect(step, result);
      if (!corrected.success) {
        return handleFailure(step, results);
      }
    }

    results.push(result);
  }

  return results;
}

4. Verify Phase

// Independent verification prevents overfitting
async function verify(step: Step, result: Result): Promise<boolean> {
  // Run tests if available
  if (step.testCommand) {
    const testResult = await runCommand(step.testCommand);
    if (!testResult.success) return false;
  }

  // Use LLM to verify against criteria
  const verification = await llmCall({
    prompt: `
      Step: ${step.description}
      Expected: ${step.successCriteria}
      Actual: ${JSON.stringify(result)}

      Does the result satisfy the success criteria?
      Respond with { "passes": boolean, "reasoning": string }
    `,
    schema: VerificationSchema
  });

  return verification.passes;
}

---

Tool Design

Tool Definition Pattern

// tools/definitions/file-operations.ts
import { z } from 'zod';

export const ReadFileTool = {
  name: 'read_file',
  description: 'Read contents of a file. Use before modifying any file.',
  parameters: z.object({
    path: z.string().describe('Absolute path to the file'),
    startLine: z.number().optional().describe('Start line (1-indexed)'),
    endLine: z.number().optional().describe('End line (1-indexed)'),
  }),
  // Risk level for guardrails (OpenAI pattern)
  riskLevel: 'low' as const,
};

export const WriteFileTool = {
  name: 'write_file',
  description: 'Write content to a file. Always read first to understand context.',
  parameters: z.object({
    path: z.string().describe('Absolute path to the file'),
    content: z.string().describe('Complete file content'),
  }),
  riskLevel: 'medium' as const,
  // Require confirmation for high-risk operations
  requiresConfirmation: true,
};

Tool Implementation

// tools/implementations/file-operations.ts
export async function readFile(
  params: z.infer<typeof ReadFileTool.parameters>
): Promise<ToolResult> {
  try {
    const content = await fs.readFile(params.path, 'utf-8');
    const lines = content.split('\n');

    const start = (params.startLine ?? 1) - 1;
    const end = params.endLine ?? lines.length;

    return {
      success: true,
      data: lines.slice(start, end).join('\n'),
      metadata: { totalLines: lines.length }
    };
  } catch (error) {
    return {
      success: false,
      error: `Failed to read file: ${error.message}`
    };
  }
}

Prefer Built-in Tools (OpenAI)

// Use platform-provided tools when available
const agent = createAgent({
  tools: [
    // Built-in tools (handled by platform)
    { type: 'web_search' },
    { type: 'code_interpreter' },

    // Custom tools only when needed
    { type: 'function', function: customDatabaseTool },
  ],
});

---

Multi-Agent Patterns

Single Agent (Default)

Use one agent for most tasks. Multiple agents add complexity.

Agent-as-Tool Pattern (OpenAI)

// Expose specialized agents as callable tools
const researchAgent = createAgent({
  name: 'researcher',
  instructions: 'You research topics and return structured findings.',
  tools: [webSearchTool, documentReadTool],
});

const mainAgent = createAgent({
  tools: [
    {
      type: 'function',
      function: {
        name: 'research_topic',
        description: 'Delegate research to specialized agent',
        parameters: ResearchQuerySchema,
        handler: async (query) => researchAgent.run(query),
      },
    },
  ],
});

Handoff Pattern (OpenAI)

// One-way transfer between agents
const customerServiceAgent = createAgent({
  tools: [
    // Handoff to specialist when needed
    {
      name: 'transfer_to_billing',
      description: 'Transfer to billing special

---

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