llm-agent-architect

Architect scalable LLM agent systems with orchestration, RAG, and observability patterns.

16|2|Updated Mar 10, 2026
One-click install
npx skills add https://github.com/jshearin01/agent-skills --skill llm-agent-architect
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-agent-architect
Source: https://github.com/jshearin01/agent-skills/tree/main/llm-agent-architect
Command: npx skills add https://github.com/jshearin01/agent-skills --skill llm-agent-architect

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Architect scalable, production-grade LLM agent systems to enable robust multi-agent coordination, tooling, memory, and observability.

Core Features & Use Cases

  • Five pillars: multi-agent coordination, RAG pipelines, tool calling, memory & state, and observability & evals.
  • Reference files provide architecture patterns and best practices for orchestrator/worker, hierarchical, sequential, parallel, swarm, and routing patterns.
  • Guidance for production readiness including memory management, context handling, versioning, and observability.

Quick Start

Provide a high-level architecture task and constraints to design a scalable LLM agent platform.

Frequently Asked Questions about llm-agent-architect

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

FAQPage Schema
How do I design a scalable multi-agent coordination architecture for LLMs?▼

To design scalable LLM agent systems, apply orchestration patterns like orchestrator/worker, hierarchical, sequential, parallel, swarm, and routing to structure multi-agent coordination and task execution.

What are the core pillars of a production-grade LLM agent architecture?▼

Production-grade LLM agent architectures rely on five pillars: multi-agent coordination, RAG pipelines, tool calling, memory and state management, and observability with continuous evaluation guardrails.

How do I implement fail-safes and stateless design in LLM agent orchestration?▼

Implement fail-safes and stateless design in LLM agent orchestration by enforcing API-first interactions, modular tool contracts, strict memory management, and continuous evaluation guardrails for production deployments.

What is the best way to structure tool contracts and memory models for LLM agents?▼

Structure tool contracts and memory models by defining modular, API-first interfaces for tool calling and implementing robust context handling and versioning to maintain state across multi-agent workflows.

How does observability work in multi-agent LLM systems?▼

Observability in multi-agent LLM systems works by integrating continuous evaluation guardrails and monitoring mechanisms to track orchestration patterns, memory handling, and tool execution for production readiness.

When should I use swarm or hierarchical routing patterns for LLM agents?▼

Use swarm or hierarchical routing patterns for LLM agents when coordinating complex, parallel multi-agent workflows that require scalable task delegation, robust fail-safes, and modular state management.