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agentic-development

Design AI agents with typed tools, guardrails, and verification tests.

Updated Jun 19, 2026
Search Tags:#ai agents#tool calling#multi-agent systems#guardrails#agent testing#pydantic ai#claude sdk
One-click install
npx skills add https://github.com/lciacci/tessera --skill agentic-development-lciacci
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agentic-development
Source: https://github.com/lciacci/tessera/tree/main/skills/agentic-development
Command: npx skills add https://github.com/lciacci/tessera --skill agentic-development-lciacci

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps developers design AI agents that can plan, use tools, manage state, enforce safety boundaries, and verify their work instead of relying on unstructured model responses.

Core Features & Use Cases

  • Framework Selection: Choose Pydantic AI for Python or the Claude Agent SDK for Node.js and Next.js agent systems.
  • Workflow Design: Implement explore-plan-execute-verify workflows with structured outputs, state management, and independent validation.
  • Tooling and Safety: Define typed tools with risk levels, guardrails, scope enforcement, human approval, and failure handling.
  • Multi-Agent Systems: Apply single-agent, agent-as-tool, handoff, and specialized-agent coordination patterns.
  • Testing and Evaluation: Create unit tests, behavior tests, mocked model tests, and end-to-end evaluations for agent reliability.

Quick Start

Ask the agentic-development skill to design a typed, tool-using AI agent for your application with an explicit plan, guardrails, state management, and verification tests.

Frequently Asked Questions about agentic-development

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build AI agents that verify their own tool calling results?▼

AI agents verify results by using explore-plan-execute-verify workflows with structured outputs, state management, and independent validation rather than relying on unstructured model responses.

What is the best way to implement guardrails and human approval for high-risk actions in multi-agent systems?▼

Guardrails for multi-agent systems are implemented by defining typed tools with risk levels, scope enforcement, and explicit human approval steps for high-risk actions to ensure safety-critical automation.

Can I use Pydantic AI and the Claude SDK for Node.js agent development?▼

Pydantic AI supports Python agent development, while the Claude Agent SDK applies to Node.js and Next.js applications, allowing framework selection based on your specific environment.

How do I test AI agent reliability and handle failures in tool-using agents?▼

Agent reliability is tested by creating unit tests, behavior tests, mocked model tests, and end-to-end evaluations, combined with explicit failure handling and typed tool schemas.

What patterns exist for coordinating specialized agents in multi-agent workflows?▼

Multi-agent workflows coordinate specialized agents using single-agent, agent-as-tool, handoff, and specialized-agent coordination patterns to manage complex state and task execution.

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