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agent-architecture

Design AI agent systems with tool integration, guardrails, and state management.

Updated Feb 18, 2026
Search Tags:#ai agents#langgraph#tool use#guardrails#episodic memory#llm orchestration
One-click install
npx skills add https://github.com/ollieb89/viflo --skill agent-architecture-ollieb89
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agent-architecture
Source: https://github.com/ollieb89/viflo/tree/main/.agent/skills/agent-architecture
Command: npx skills add https://github.com/ollieb89/viflo --skill agent-architecture-ollieb89

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a comprehensive guide and practical examples for designing, implementing, and deploying sophisticated AI agent systems, addressing common pitfalls like runaway costs and data integrity.

Core Features & Use Cases

  • Tool-Using Agents: Implement agents that can interact with external tools and APIs.
  • Guardrails & Safety: Enforce critical safety measures like turn limits and token caps to prevent cost overruns and ensure predictable behavior.
  • Streaming & Real-time UX: Set up efficient streaming responses for a better user experience.
  • Multi-Agent Orchestration: Design complex workflows using LangGraph for advanced agent coordination.
  • Memory & State Management: Implement episodic memory using vector databases for persistent recall.
  • Use Case: Develop a customer support agent that can fetch user data from a database, interact with an external knowledge base, and provide summarized answers, all while staying within budget and maintaining data privacy.

Quick Start

Use the agent-architecture skill to run a tool-using agent with mandatory guardrails by fetching the content of https://example.com and summarising it.

Frequently Asked Questions about agent-architecture

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

FAQPage Schema
How do I build AI agents with guardrails to prevent cost overruns?▼

Build AI agents with guardrails by enforcing turn limits and token caps to prevent cost overruns and ensure predictable behavior. This approach addresses common production failure modes like runaway costs and data integrity issues.

How do I implement multi-agent orchestration with LangGraph?▼

Implement multi-agent orchestration with LangGraph by designing complex workflows for advanced agent coordination. The architecture provides patterns for managing state and routing between multiple specialized agents.

Does this AI agent architecture support episodic memory with pgvector?▼

Yes, this AI agent architecture supports episodic memory with pgvector. It implements memory and state management solutions using vector databases for persistent recall across agent interactions.

Can I use FastAPI and Next.js for streaming AI agent responses?▼

Yes, you can use FastAPI and Next.js for streaming AI agent responses. The architecture provides code examples in Python and TypeScript to set up efficient streaming responses for real-time user experiences.

What's the best way to integrate external tools with LLM orchestration?▼

The best way to integrate external tools with LLM orchestration is using tool-using agents that interact with external APIs. This architecture guides tool integration alongside robust state management for production deployments.

Why do AI agents fail in production and how do I handle limitations?▼

AI agents fail in production due to runaway costs, data integrity issues, and unbounded execution. Handle these limitations by implementing mandatory guardrails, turn limits, and token caps for predictable behavior.

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