agentic-development

This skill teaches you to architect and deploy autonomous agents capable of reasoning, planning, and executing complex tasks with external tools. Learn framework selection by language—Pydantic AI for Python's type safety, Claude Agent SDK for Node.js streaming—plus the explore-plan-execute-verify workflow pattern that keeps agents grounded and verifiable.

Agentic Development helps you build autonomous AI agents using Pydantic AI for Python or Claude Agent SDK for Node.js.

AI-generated summary based on this skill's SKILL.md

★ 703 ⁮ 56 MIT updated 2026-07-14 by alinaqi

Install

alinaqi/maggy/agentic-development · repository language: Python

Manual (git clone) CLI (skillfed)coming soon

git clone https://github.com/alinaqi/maggy
cp -r maggy/skills/agentic-development ~/.claude/skills/agentic-development

Frequently asked questions

AI-generated answers based on this skill's SKILL.md and metadata

How to build AI agents with Python using Pydantic AI?

Agentic-development teaches you to build autonomous AI agents in Python using Pydantic AI, which provides type-safe tool definitions and reasoning loops. You'll learn to structure agents with clear schemas, implement the explore-plan-execute-verify workflow, and integrate external tools for complex task execution while maintaining safety and verifiability throughout the agent's reasoning process.

What's the difference between Pydantic AI and Claude SDK for agents?

Agentic-development compares both frameworks: Pydantic AI excels in Python with strong type safety and schema validation, while Claude Agent SDK is optimized for Node.js with native streaming support. The skill helps you choose based on your language preference and requirements—Python for type-driven development, Node.js for real-time streaming agent responses and modern JavaScript ecosystems.

How do you implement multi-step autonomous agent workflows?

Agentic-development covers the explore-plan-execute-verify pattern for multi-step workflows. You'll learn to decompose complex tasks into stages where agents explore the problem space, create plans, execute tool calls, and verify results before proceeding. This structured approach ensures agents remain grounded, traceable, and capable of handling intricate reasoning tasks with external tool integration.

What guardrails and safety measures protect autonomous agents?

Agentic-development emphasizes adding safety guardrails and human oversight to agents. You'll implement verification steps in workflows, define tool schemas with proper constraints, establish execution boundaries, and design handoff patterns for specialized tasks. These practices prevent agent drift, ensure tool calls stay within intended scope, and maintain human control over critical decisions.

How do you design and test agent tools with proper schemas?

Agentic-development teaches tool design through schema-first approaches, ensuring each tool has clear input validation, output specifications, and safety constraints. You'll learn to test tools independently before agent integration, verify function calling behavior, and use type systems (especially Pydantic's validation) to catch errors early. Proper schemas make tools predictable and agents more reliable.

What agent patterns handle memory and state management?

Agentic-development covers agent memory systems including context windows, persistent state storage, and conversation history management. You'll explore patterns for tracking agent decisions across steps, maintaining tool execution history, and designing specialized agent handoffs. These techniques enable agents to reason over time, learn from past interactions, and coordinate complex multi-step workflows effectively.

SKILL.md

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