Multi-Agent Hierarchy Decision Framework
SkillSkill
Decision matrix for the skill vs sub-agent question. Research-backed across 50+ sources. Core finding: deep hierarchies fail 41-87% of the time in production.
About
A decision framework for the most consequential architecture question in multi-agent system design: should this capability be a skill (tool call) or a sub-agent?
Based on synthesis of 50+ sources from 2025-2026 across CrewAI, AutoGen, LangGraph, Magentic-One, Claude Agent SDK, Google A2A, MetaGPT, and production case studies. Core finding: deep agent hierarchies (3+ tiers) fail in production 41-87% of the time. A flat orchestrator with skilled specialists handles 90% of real-world requirements.
The decision matrix uses 7 signals: task determinism, step count, error recovery complexity, latency requirements, token cost tolerance, iteration need, and context sharing requirements. Also covers the 'agent as a tool' pattern, which wraps a complex agent call as a skill and provides sub-agent capability without architectural commitment.
What is included: the full framework document, the 7-signal decision matrix, failure mode analysis from production case studies, the agent-as-a-tool wrapper pattern with code example, and the T2+tools formula.
Best for anyone designing a multi-agent system before they build it, or restructuring one that has become brittle.
Core Capabilities
- 7-signal decision matrix: when to use skills vs sub-agents vs deep hierarchies
- T2+tools architecture pattern — the formula that covers 90% of production requirements
- The 'agent as a tool' wrapper pattern — sub-agent capability without architectural debt
- Failure mode analysis: why T3+ hierarchies fail in production (41–87% rate)
- Research synthesis from CrewAI, AutoGen, LangGraph, Claude Agent SDK, Magentic-One
- Token cost comparison: skills vs sub-agents (2–5x multiplier breakdown)
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Version History
This skill is actively maintained.
March 25, 2026
One-time purchase
$29
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Creator
Melisia Archimedes
Creator
Melisia Archimedes is the architect behind the Hive Doctrine — a production-tested system for building, orchestrating, and running multi-agent AI teams. I've spent years in the field, not on the whiteboard. Every config, framework, and pattern I sell has run in a live production environment managing real workflows, real decisions, and real money. What's in the Hive Doctrine isn't theory — it's what survived contact with reality. My work spans agent identity design, memory architecture, multi-agent coordination, and the operator systems that hold everything together under pressure. The Pantheon agents — Marcus, Elliott, Elijah, Lila, Priya, and the rest — are production personas I built for my own operation and now make available to serious operators who want a real foundation instead of a blank prompt. If you're tired of starting from scratch every time, these configs will cut your setup time from weeks to hours and give you a system that actually holds together at scale.
View creator profile →Details
- Type
- Skill
- Category
- Engineering
- Price
- $29
- Version
- 1
- License
- One-time purchase
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