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DeepAgents Architecture

Design AI agents with scalable architecture using Team Topologies principles.

2|Updated Jan 15, 2026
Search Tags:#AI agents#modular design#agent architecture#DeepAgents#Team Topologies
One-click install
npx skills add https://github.com/spulido99/claude-toolkit --skill deepagents-architecture-spulido99
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: DeepAgents Architecture
Source: https://github.com/spulido99/claude-toolkit/tree/main/plugins/deepagents-builder/skills/architecture
Command: npx skills add https://github.com/spulido99/claude-toolkit --skill deepagents-architecture-spulido99

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of designing and implementing complex AI agents that can scale, remain maintainable, and adapt to evolving business needs.

Core Features & Use Cases

  • Capability-First Design: Organizes agents around business capabilities rather than technical implementation, promoting modularity and flexibility.
  • Bounded Contexts: Ensures each agent has a clear, well-defined scope, reducing cognitive load and enabling independent evolution.
  • Cognitive Load Management: Offers guidelines for determining the optimal number of tools and subagents to avoid overwhelming the main agent.
  • Agent-Native Principles: Promotes parity between UI features and agent capabilities, granular tool design, and composability for emergent capability.
  • Data Architecture: Provides guidance on when to use files versus databases and how to manage context with AGENTS.md files.
  • Agent Topologies: Offers patterns for organizing agents into orchestrators, platforms, specialized subsystems, and enabling agents.
  • Validation Checklist: Ensures that the architecture meets all necessary criteria before finalization.
  • Quick Patterns: Provides templates for common agent architecture patterns, including simple stream-aligned, platform-supported, and domain-specialized.
  • Design Process: Guides through mapping business capabilities, defining bounded contexts, designing subagent topology, and defining interaction modes.
  • Real-World Examples: Offers practical examples of agent architecture in different domains.
  • Capability Maturity: Helps map capability maturity to agent sophistication for continuous improvement.
  • Template: Offers a template for new mappings to simplify the process.

Quick Start

Use the create_deep_agent function to design your AI agent, defining its capabilities, bounded contexts, and tools based on the principles outlined in this Skill.

Frequently Asked Questions about DeepAgents Architecture

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

FAQPage Schema
How do I design AI agent architecture for scalability and maintainability?▼

Designing scalable AI agent architecture involves organizing agents around business capabilities using Team Topologies principles. This approach promotes modularity by defining clear bounded contexts and managing cognitive load, ensuring agents remain adaptable to evolving business needs without becoming overwhelming.

What is the best way to structure AI agents with bounded contexts?▼

Structuring AI agents with bounded contexts requires mapping business capabilities to clear, well-defined scopes. This reduces cognitive load by limiting the tools and subagents each main agent manages, enabling independent evolution and preventing overlap in complex data analysis or customer support scenarios.

How do I manage cognitive load when building modular AI agents?▼

Managing cognitive load in modular AI agents requires guidelines for determining the optimal number of tools and subagents. By organizing agent topologies into orchestrators, platforms, and specialized subsystems, you prevent overwhelming the main agent and maintain system maintainability.

Do I need to understand Team Topologies to build AI agents?▼

Understanding Team Topologies principles is required to build AI agents with this architecture approach. The design process applies these principles to map capabilities, define bounded contexts, design subagent topology, and define interaction modes for continuous capability maturity improvement.

When should I use files versus databases in AI agent data architecture?▼

AI agent data architecture provides specific guidance on when to use files versus databases. This decision impacts how you manage context with AGENTS.md files, ensuring efficient data handling and capability parity between UI features and agent capabilities.

What patterns exist for organizing AI agent topologies?▼

AI agent topologies offer patterns for organizing agents into orchestrators, platforms, specialized subsystems, and enabling agents. Quick patterns provide templates for common architectures, including simple stream-aligned, platform-supported, and domain-specialized configurations for various real-world examples.

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