🚀 The LLM Zoo in 2026: How not to go crazy?
Today, opening your AI toolbox means facing a dizzying abundance: GPT-4.1, Claude 4 Sonnet, DeepSeek-V3, Gemini 2.5... Choosing the right model for the right task has become a software engineering problem in itself.
❌ The problem: Manual selection is unmanageable. Worse, burning a super-premium, expensive model to format a simple docstring or fix a typo is an economic non-sense.
💡 The solution: Delegate that choice to an AI agent via an intelligent routing and orchestration pattern. A lightweight, fast local model classifies intent upfront, then dispatches the task to the best-suited specialist model.
🛠️ The weekend prototype: To test this out, I hacked together AI Dispatch, a local MCP (Model Context Protocol) server in TypeScript. It lets you configure specialized agents and chain complex workflows (DAGs) transparently.
Curious about the architecture or config? I wrote down a full breakdown of this weekend experiment in my latest blog post.
👉 Read the full article here: https://blog.mornati.net/the-ai-orchestrator-why-intelligent-delegation-is-the-missing-piece-in-your-ai-toolchain
#SoftwareEngineering #ArtificialIntelligence #AIAgents #MCP #TypeScript #OpenSource #TechArchitecture #LLM #DevLife

The AI Orchestrator: Why Intelligent Delegation is the Missing Piece in Your AI Toolchain
1. Introduction: The Age of Model Abundance The AI assistant landscape in mid-2026 is one of abundance. According to McKinsey's State of AI report, 78% of organizations now use AI regularly, and the n








